What do we know about eye movements in Alzheimer's disease? The past 37 years and future directions

Low-level factors increase gaze-guidance under cognitive load: A comparison of image-salience and semantic-salience models

Growing evidence links eye movements and cognitive functioning, however there is debate concerning what image content is fixated in natural scenes. Competing approaches have argued that low-level/feedforward and high-level/feedback factors contribute to gaze-guidance. We used one low-level model (Graph Based Visual Salience, GBVS) and a novel language-based high-level model (Global Vectors for Word Representation, GloVe) to predict gaze locations in a natural image search task, and we examined how fixated locations during this task vary under increasing levels of cognitive load. Participants (N = 30) freely viewed a series of 100 natural scenes for 10 seconds each. Between scenes, subjects identified a target object from the scene a specified number of trials (N) back among three distracter objects of the same type but from alternate scenes. The N-back was adaptive: N-back increased following two correct trials and decreased following one incorrect trial. Receiver operating characteristic (ROC) analysis of gaze locations showed that as cognitive load increased, there was a significant increase in prediction power for GBVS, but not for GloVe. Similarly, there was no significant difference in the area under the ROC between the minimum and maximum N-back achieved across subjects for GloVe (t(29) = -1.062, p = 0.297), while there was a cohesive upwards trend for GBVS (t(29) = -1.975, p = .058), although not significant. A permutation analysis showed that gaze locations were correlated with GBVS indicating that salient features were more likely to be fixated. However, gaze locations were anti-correlated with GloVe, indicating that objects with low semantic consistency with the scene were more likely to be fixated. These results suggest that fixations are drawn towards salient low-level image features and this bias increases with cognitive load. Additionally, there is a bias towards fixating improbable objects that does not vary under increasing levels of cognitive load.

Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches

Eye movement assessments have the potential to help in diagnosis and tracking of neurological disorders. Cerebellar ataxias cause profound and characteristic abnormalities in smooth pursuit, saccades, and fixation. Oculomotor dysmetria (i.e., hypermetric and hypometric saccades) is a common finding in individuals with cerebellar ataxia. In this study, we evaluated a scalable approach for detecting and quantifying oculomotor dysmetria. Eye movement data were extracted from iPhone video recordings of the horizontal saccade task (a standard clinical task in ataxia) and combined with signal processing and machine learning approaches to quantify saccade abnormalities. Entropy-based measures of eye movements during saccades were significantly different in 72 individuals with ataxia with dysmetria compared with 80 ataxia and Parkinson's participants without dysmetria. A template matching-based analysis demonstrated that saccadic eye movements in patients without dysmetria were more similar to the ideal template of saccades. A support vector machine was then used to train and test the ability of multiple signal processing features in combination to distinguish individuals with and without oculomotor dysmetria. The model achieved 78% accuracy (sensitivity= 80% and specificity= 76%). These results show that the combination of signal processing and machine learning approaches applied to iPhone video of saccades, allow for extraction of information pertaining to oculomotor dysmetria in ataxia. Overall, this inexpensive and scalable approach for capturing important oculomotor information may be a useful component of a screening tool for ataxia and could allow frequent at-home assessments of oculomotor function in natural history studies and clinical trials.

Affective Computing for Late-Life Mood and Cognitive Disorders

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.

The Contribution of Ocular Biomarkers in the Differential Diagnosis of Alzheimer's Disease versus Other Types of Dementia and Future Prospects

With dementia becoming increasingly prevalent, there is a pressing need to become better equipped with accurate diagnostic tools that will favorably influence its course via prompt and specific intervention. The overlap in clinical manifestation, imaging, and even pathological findings between different dementia syndromes is one of the most prominent challenges today even for expert physicians. Since cerebral microvasculature and the retina share common characteristics, the idea of identifying potential ocular biomarkers to facilitate diagnosis is not a novel one. Initial efforts included studying less quantifiable parameters such as aspects of visual function, extraocular movements, and funduscopic findings. However, the really exciting prospect of a non-invasive, safe, fast, reproducible, and quantifiable method of pinpointing novel biomarkers has emerged with the advent of optical coherence tomography (OCT) and, more recently, OCT angiography (OCTA). The possibility of analyzing multiple parameters of retinal as well as retinal microvasculature variables in vivo represents a promising opportunity to investigate whether specific findings can be linked to certain subtypes of dementia and aid in their earlier diagnosis. The existing literature on the contribution of the eye in characterizing dementia, with a special interest in OCT and OCTA parameters will be reviewed and compared, and we will explicitly focus our effort in advancing our understanding and knowledge of relevant biomarkers to facilitate future research in the differential diagnosis between Alzheimer's disease and common forms of cognitive impairment, including vascular dementia, frontotemporal dementia, and dementia with Lewy bodies.

Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest

Medical research shows that eye movement disorders are related to many kinds of neurological diseases. Eye movement characteristics can be used as biomarkers of Parkinson's disease, Alzheimer's disease (AD), schizophrenia, and other diseases. However, due to the unknown medical mechanism of some diseases, it is difficult to establish an intuitive correspondence between eye movement characteristics and diseases. In this paper, we propose a disease classification method based on decision tree and random forest (RF). First, a variety of experimental schemes are designed to obtain eye movement images, and information such as pupil position and area is extracted as original features. Second, with the original features as training samples, the long short-term memory (LSTM) network is used to build classifiers, and the classification results of the samples are regarded as the evolutionary features. After that, multiple decision trees are built according to the C4.5 rules based on the evolutionary features. Finally, a RF is constructed with these decision trees, and the results of disease classification are determined by voting. Experiments show that the RF method has good robustness and its classification accuracy is significantly better than the performance of previous classifiers. This study shows that the application of advanced artificial intelligence (AI) technology in the pathological analysis of eye movement has obvious advantages and good prospects.

Visual Features in Alzheimer's Disease: From Basic Mechanisms to Clinical Overview

Alzheimer's disease (AD) is the leading cause of dementia worldwide. It compromises patients' daily activities owing to progressive cognitive deterioration, which has elevated direct and indirect costs. Although AD has several risk factors, aging is considered the most important. Unfortunately, clinical diagnosis is usually performed at an advanced disease stage when dementia is established, making implementation of successful therapeutic interventions difficult. Current biomarkers tend to be expensive, insufficient, or invasive, raising the need for novel, improved tools aimed at early disease detection. AD is characterized by brain atrophy due to neuronal and synaptic loss, extracellular amyloid plaques composed of amyloid-beta peptide (Aβ), and neurofibrillary tangles of hyperphosphorylated tau protein. The visual system and central nervous system share many functional components. Thus, it is plausible that damage induced by Aβ, tau, and neuroinflammation may be observed in visual components such as the retina, even at an early disease stage. This underscores the importance of implementing ophthalmological examinations, less invasive and expensive than other biomarkers, as useful measures to assess disease progression and severity in individuals with or at risk of AD. Here, we review functional and morphological changes of the retina and visual pathway in AD from pathophysiological and clinical perspectives.

A review of biomarkers of Alzheimer's disease in noninvasive samples

The discovery of biomarkers that confer high confidence of presymptomatic Alzheimer's disease (AD) diagnosis would be a valuable tool to study the etiology of the disease, to find risk factors, to discover more treatments and medicines. The present work reviews the potential biomarkers of AD based on the concentration changes of small molecules and chemical elements in noninvasive samples (urine, saliva, hair and others). An updated table with 74 target compounds is produced and ranked. Until the present date, there are a few biomarkers, present in urine, with the most promising potential: isoprostane 8,12-iso-iPF2a-VI, total free amino acids, 8-hydroxy-2'-deoxyguanosine, glycine and enzymatic activity of NaCl-stimulated PON1. All show increased levels in AD carriers, with the exception of NaCl-stimulated PON1.

Computational Techniques for Eye Movements Analysis towards Supporting Early Diagnosis of Alzheimer's Disease: A Review

An opportune early diagnosis of Alzheimer's disease (AD) would help to overcome symptoms and improve the quality of life for AD patients. Research studies have identified early manifestations of AD that occur years before the diagnosis. For instance, eye movements of people with AD in different tasks differ from eye movements of control subjects. In this review, we present a summary and evolution of research approaches that use eye tracking technology and computational analysis to measure and compare eye movements under different tasks and experiments. Furthermore, this review is targeted to the feasibility of pioneer work on developing computational tools and techniques to analyze eye movements under naturalistic scenarios. We describe the progress in technology that can enhance the analysis of eye movements everywhere while subjects perform their daily activities and give future research directions to develop tools to support early AD diagnosis through analysis of eye movements.