Eye-tracking indices of impaired encoding of visual short-term memory in familial Alzheimer's disease

Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses

Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), frequently exhibit deficits in executive function and visuospatial memory. Traditional assessments, such as the Rey‒Osterrieth Complex Figure Test (RCFT), performed in clinical settings require time and effort. This study aimed to develop a deep learning model using the RCFT and based on eye tracking to detect impaired executive function during visuospatial memory encoding in patients with mental illnesses. In 96 patients with first-episode psychosis, 49 with clinical high risk for psychosis, 104 with OCD, and 159 healthy controls, eye movements were recorded during a 3-min RCFT figure memorization task, and organization and immediate recall scores were obtained. These scores, along with the fixation points indicating eye-focused locations in the figure, were used to train a Long Short-Term Memory + Attention model for detecting impaired executive function and visuospatial memory. The model distinguished between normal and impaired executive function, with an F1 score of 83.5%, and identified visuospatial memory deficits, with an F1 score of 80.7%, regardless of psychiatric diagnosis. These findings suggest that this eye tracking-based deep learning model can directly and rapidly identify impaired executive function during visuospatial memory encoding, with potential applications in various psychiatric and neurological disorders.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.

A novel eye-movement impairment in multiple sclerosis indicating widespread cortical damage

In multiple sclerosis, remyelination trials have yet to deliver success like that achieved for relapse rates with disease course modifying treatment trials. The challenge is to have a clinical, functional outcome measure. Currently, there are none that have been validated, other than visual evoked potentials in optic neuritis. Like vision, quick eye movements (saccades) are heavily dependent on myelination. We proposed that it is possible to extrapolate from demyelination of the medial longitudinal fasciculus in the brainstem to quantitative assessment of cortical networks governing saccadic eye movements in multiple sclerosis. We have developed and validated a double-step saccadic test, which consists of a pair of eye movements towards two stimuli presented in quick succession (the demonstrate eye movement networks with saccades protocol). In this single-centre, cross-sectional cohort study we interrogated the structural and functional relationships of double-step saccades in multiple sclerosis. Data were collected for double-step saccades, cognitive function (extended Rao's Brief Repeatable Battery), disability (Expanded Disability Status Scale) and visual functioning in daily life (National Eye Institute Visual Function Questionnaire). MRI was used to quantify grey matter atrophy and multiple sclerosis lesion load. Multivariable linear regression models were used for analysis of the relationships between double-step saccades and clinical and MRI metrics. We included 209 individuals with multiple sclerosis (mean age 54.3 ± 10.5 years, 58% female, 63% relapsing-remitting multiple sclerosis) and 60 healthy control subjects (mean age 52.1 ± 9.2 years, 53% female). The proportion of correct double-step saccades was significantly reduced in multiple sclerosis (mean 0.29 ± 0.22) compared to controls (0.45 ± 0.22, P < 0.001). Consistent with this, there was a significantly larger double-step dysmetric saccadic error in multiple sclerosis (mean vertical error -1.18 ± 1.20°) compared to controls (-0.54 ± 0.86°, P < 0.001). Impaired double-step saccadic metrics were consistently associated with more severe global and local grey matter atrophy (correct responses-cortical grey matter: β = 0.42, P < 0.001), lesion load (vertical error: β = -0.28, P < 0.001), progressive phenotypes, more severe physical and cognitive impairment (correct responses-information processing: β = 0.46, P < 0.001) and visual functioning. In conclusion, double-step saccades represent a robust metric that revealed a novel eye-movement impairment in individuals with multiple sclerosis. Double-step saccades outperformed other saccadic tasks in their statistical relationship with clinical, cognitive and visual functioning, as well as global and local grey matter atrophy. Double-step saccades should be evaluated longitudinally and tested as a potential novel outcome measure for remyelination trials in multiple sclerosis.

Eyes on dementia: an overview of the interplay between eye movements and cognitive decline

The economic and disease burden of dementia is forecasted to continue increasing. Considering its cognitive effects, timely diagnosis is important in developing a stage-based treatment plan and gathering data to support advocacy efforts and plan healthcare and social services. Eye-tracking technology has emerged as an efficient diagnostic tool in clinical practice and experimental studies. This review aimed to comprehensively analyze various aspects of eye-tracking technology, including pupillometry parameters, eye movements, eye-tracking devices, and neuropsychological tools. We conducted a systematic review retrieving articles published in the last ten years from six databases. Our results provide a complex overview for each included form of dementia/cognitive decline in terms of patient characteristics (age, sex-disaggregated by included pathologies), inclusion and exclusion criteria, devices, and neuropsychological tools. We also summarized findings on fixation stability tasks, saccadic evaluation, pupillometry, scene perception, object recognition, spatial memory, eye-tracking video tasks, and visual search. The eye-tracking method has become more common in cognitive assessments.

A single-center, randomized, parallel design study to evaluate the efficacy of donepezil in improving visuospatial abilities in patients with mild cognitive impairment using eye-tracker: the COG-EYE study protocol for a phase II trial

Background

Cholinesterase inhibitors (ChEIs) decrease long-term cognitive decline in patients with Alzheimer’s disease (AD); however, there is little evidence that ChEIs affect cognitive test scores in patients with mild cognitive impairment (MCI). Conventional endpoints, such as cognitive tests or clinical rating scores, may lack the sensitivity to subtle treatment effects in participants with MCI. Therefore, there is an immediate need to refocus on direct physiological assessments to detect the effects of ChEIs in patients with MCI due to AD.

Methods

We propose a randomized controlled trial to evaluate the effect of donepezil, a ChEI, on patients with MCI due to AD. We plan to recruit 78 participants (39 in each arm) with MCI who had amyloid positron emission tomography (PET)-positive results for this open-label study. To evaluate subtle differences, we will measure eye-tracking metrics and digital pen data while participants perform the simplified Rey Complex Figure (RCFT) and clock drawing tests. The primary outcome is a change in the ratio of the number of fixations (working space/perceptual space) performed using the simplified RCFT, from baseline to 12 weeks, as assessed using eye-tracking metrics. The secondary outcomes are changes in general cognition, clinical severity, activities of daily living, and visuospatial function assessed using standard rating scores and digital pen data. The analyses of the primary and secondary outcomes will be based on the difference in changes during follow-up between the donepezil and control groups using the t-test or Mann–Whitney U test, as well as adjusting for baseline values.

Discussion

This study is designed to determine whether eye-tracking metrics can detect the effect of donepezil on visuospatial dysfunction more sensitively in patients with MCI. It is expected that multimodal data, such as eye-tracking and digital pen data, may provide helpful biomarkers for identifying subtle changes that are difficult to measure using conventional methods.

Trial registration

Clinical Research Information Service, Republic of Korea (CRIS, cris.nih.go.kr) KCT0006236. Registered on June 10, 2021.

A novel deep learning approach for diagnosing Alzheimer's disease based on eye-tracking data

Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks.

Aging changes the interactions between the oculomotor and memory systems

The use of multi-modal approaches, particularly in conjunction with multivariate analytic techniques, can enrich models of cognition, brain function, and how they change with age. Recently, multivariate approaches have been applied to the study of eye movements in a manner akin to that of neural activity (i.e., pattern similarity). Here, we review the literature regarding multi-modal and/or multivariate approaches, with specific reference to the use of eyetracking to characterize age-related changes in memory. By applying multi-modal and multivariate approaches to the study of aging, research has shown that aging is characterized by moment-to-moment alterations in the amount and pattern of visual exploration, and by extension, alterations in the activity and function of the hippocampus and broader medial temporal lobe (MTL). These methodological advances suggest that age-related declines in the integrity of the memory system has consequences for oculomotor behavior in the moment, in a reciprocal fashion. Age-related changes in hippocampal and MTL structure and function may lead to an increase in, and change in the patterns of, visual exploration in an effort to upregulate the encoding of information. However, such visual exploration patterns may be non-optimal and actually reduce the amount and/or type of incoming information that is bound into a lasting memory representation. This research indicates that age-related cognitive impairments are considerably broader in scope than previously realized.

Salient Cognitive Paradigms to Assess Preclinical Alzheimer's Disease

Despite the growing emphasis to identify early biological markers that can detect the progressive accumulation of brain pathology in the complex pathophysiologic cascade that occurs in Alzheimer's disease (AD), we continue to employ the same neuropsychological paradigms that were developed to detect dementia or frank cognitive impairment. It has become increasingly clear that we cannot expect to measure clinically meaningful change in relationship to these emerging preclinical biomarkers using these traditional cognitive assessment paradigms, nor will we advance the efforts to identify the earliest cognitive changes that emerge in AD. Over the last decade, a few novel promising cognitive assessment paradigms have emerged that have shown promise in identifying subtle cognitive deficits in AD which aids in early detection and monitoring of meaningful cognitive change over time. Some of these cognitive assessment paradigms are reviewed here, including semantic interference, semantic intrusion errors, memory binding, and binding of face and name associations. These paradigms may be useful for AD clinical trials focused on secondary prevention if there is sufficient rigor to suggest that they correlate with AD biomarkers, having robust sensitivity, specificity, and predictive utility among culturally and linguistically diverse populations at-risk for AD.

Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network

One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.