Multimodal visual exploration disturbances in Parkinson's disease detected with an infrared eye-movement assessment system

Detecting Abnormal Eye Movements in Patients with Neurodegenerative Diseases - Current Insights

This review delineates the ocular motor disturbances across a spectrum of neurodegenerative disorders, including Alzheimer's Disease (AD) and related disorders (ADRD), Parkinson's Disease (PD), atypical parkinsonism, and others, leveraging advancements in eye-tracking technology for enhanced diagnostic precision. We delve into the different classes of eye movements, their clinical assessment, and specific abnormalities manifesting in these diseases, highlighting the nuanced differences and shared patterns. For instance, AD and ADRD are characterized by increased saccadic latencies and instability in fixation, while PD features saccadic hypometria and mild smooth pursuit impairments. Atypical parkinsonism, notably Progressive Supranuclear Palsy (PSP) and Corticobasal Syndrome (CBS), presents with distinct ocular motor signatures such as vertical supranuclear gaze palsy and saccadic apraxia, respectively. Our review underscores the diagnostic value of eye movement analysis in differentiating between these disorders and also posits the existence of underlying common pathological mechanisms. We discuss how eye movements have potential as biomarkers for neurodegenerative diseases but also some of the existing limitations.

Abnormal eye movements in Parkinson's disease: From experimental study to clinical application

Parkinson's disease (PD) is a neurodegenerative disease that integrates a series of motor symptoms and non-motor symptoms, making early recognition challenging. The exploration of biomarkers is urgently required. Abnormal eye movements in PD have been reported to appear in a variety of ways since eye tracking technology was developed, such as decreased saccade amplitude, extended saccade latency, and unique saccade patterns. Non-invasive, objective and simple eye tracking has the potential to provide effective biomarkers for the PD diagnosis, progression and cognitive impairment, as well as ideas for research into the occurrence and treatment strategy of motor symptoms. In this review, we introduced the fundamental eye movement patterns and typical eye movement paradigms (such as fixation, pro-saccade, anti-saccade, smooth tracking, and visual search), summarized the symptoms of various ocular motor abnormalities in PD, and discussed the research implications of oculomotor investigation to the pathogenesis of PD and related motor symptoms, as well as the clinical implications as biomarkers and its inspiration on treatment.

Gaze and Environmental Context-Guided Deep Neural Network and Sequential Decision Fusion for Grasp Intention Recognition

Grasp intention recognition plays a crucial role in controlling assistive robots to aid older people and individuals with limited mobility in restoring arm and hand function. Among the various modalities used for intention recognition, the eye-gaze movement has emerged as a promising approach due to its simplicity, intuitiveness, and effectiveness. Existing gaze-based approaches insufficiently integrate gaze data with environmental context and underuse temporal information, leading to inadequate intention recognition performance. The objective of this study is to eliminate the proposed deficiency and establish a gaze-based framework for object detection and its associated intention recognition. A novel gaze-based grasp intention recognition and sequential decision fusion framework (GIRSDF) is proposed. The GIRSDF comprises three main components: gaze attention map generation, the Gaze-YOLO grasp intention recognition model, and sequential decision fusion models (HMM, LSTM, and GRU). To evaluate the performance of GIRSDF, a dataset named Invisible containing data from healthy individuals and hemiplegic patients is established. GIRSDF is validated by trial-based and subject-based experiments on Invisible and outperforms the previous gaze-based grasp intention recognition methods. In terms of running efficiency, the proposed framework can run at a frequency of about 22 Hz, which ensures real-time grasp intention recognition. This study is expected to inspire additional gaze-related grasp intention recognition works.

A novel tablet-based software for the acquisition and analysis of gaze and eye movement parameters: a preliminary validation study in Parkinson's disease

The idea that eye movements can reflect certain aspects of brain function and inform on the presence of neurodegeneration is not a new one. Indeed, a growing body of research has shown that several neurodegenerative disorders, such as Alzheimer's and Parkinson's Disease, present characteristic eye movement anomalies and that specific gaze and eye movement parameters correlate with disease severity. The use of detailed eye movement recordings in research and clinical settings, however, has been limited due to the expensive nature and limited scalability of the required equipment. Here we test a novel technology that can track and measure eye movement parameters using the embedded camera of a mobile tablet. We show that using this technology can replicate several well-known findings regarding oculomotor anomalies in Parkinson's disease (PD), and furthermore show that several parameters significantly correlate with disease severity as assessed with the MDS-UPDRS motor subscale. A logistic regression classifier was able to accurately distinguish PD patients from healthy controls on the basis of six eye movement parameters with a sensitivity of 0.93 and specificity of 0.86. This tablet-based tool has the potential to accelerate eye movement research via affordable and scalable eye-tracking and aid with the identification of disease status and monitoring of disease progression in clinical settings.

Antisaccades in Parkinson's Disease: A Meta-Analysis

The usefulness of eye-tracking tasks as potential biomarkers for motor or cognitive disease burden in Parkinson's disease (PD) has been subject of debate for many years. Several studies suggest that the performance in the antisaccade task may be altered in patients with PD and associated with motor disease severity or executive dysfunction. In this meta-analysis, random effects models were used to synthesize the existing evidence on antisaccade error rates and latency in PD. Furthermore, meta-regressions were performed to assess the role of motor and cognitive disease severity, dopaminergic medication and methodological factors. Additionally, the impact of acute levodopa administration and activation of deep brain stimulation was evaluated in two separate sub-analyses.This meta-analysis confirms that antisaccade latency and error rate are significantly increased in PD. Disease duration, Unified Parkinson's disease rating scale score and Hoehn and Yahr stage mediate the effect of PD on antisaccade latency with higher motor burden being associated with increased antisaccade latency.Acute administration of levodopa had no significant effects on antisaccade performance in a small number of eligible studies. Deep brain stimulation in the subthalamic nucleus, on the other hand, may alter the speed accuracy trade-off supporting an increase of impulsivity following deep brain stimulation in PD.According to the results of the meta-analysis, antisaccade latency may provide a potential marker for disease severity and progression in PD which needs further confirmation in longitudinal studies.