Focus group reflections on the current and future state of cognitive assessment tools in geriatric health care

The application of saccades to assess cognitive impairment among older adults: a systematic review and meta-analysis

BACKGROUND

Saccade is a novel and feasible method for cognition assessment and has potential to screen older people with cognitive impairment.

OBJECTIVES

To systematically summarize the evidence and determine whether different saccade parameters can effectively identify patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).

METHODS

English and Chinese databases were searched until 19 April 2022. Studies analyzing saccade parameters in older adults with normal cognition, MCI, or AD were included. Two researchers independently performed the screening, data extraction, and quality appraisal. Meta-analyses were conducted and standard mean differences and 95% confidence intervals were estimated with a random effects model.

RESULTS

Thirty-five studies were included, and 26 studies were pooled for the meta-analysis. The results demonstrated that patients with cognitive impairment exhibited longer latency and lower accuracy rates in the prosaccade and antisaccade tasks, along with lower corrected error rates in the antisaccade tasks. However, the pooled results for antisaccades were more stable, providing the ability to distinguish patients with cognitive impairment among older adults. The results of the subgroup analyses revealed that only the accuracy rates of the antisaccades differed significantly between people with MCI and AD. Regarding the differences between older adults with normal cognition and those with MCI, the effect sizes of latency and the accuracy rates of saccades as well as the corrected error rates of antisaccades were significant.

CONCLUSIONS

Saccades, especially antisaccades, are a potential screening and assessment tool for distinguishing older adults with MCI or AD from those with normal cognition.

Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment

OBJECTIVE

Alzheimer's disease (AD) is a neurodegenerative disorder that initially presents with memory loss in the presence of underlying neurofibrillary tangle and amyloid plaque pathology. Mild cognitive impairment is the initial symptomatic stage, which is an early window for detecting cognitive impairment prior to progressive decline and dementia. We recently developed the Visuospatial Memory Eye-Tracking Test (VisMET), a passive task capable of classifying cognitive impairment in AD in under five minutes. Here we describe the development of a mobile version of VisMET to enable efficient and widespread administration of the task.

METHODS

We delivered VisMET on iPad devices and used a transfer learning approach to train a deep neural network to track eye gaze. Eye movements were used to extract memory features to assess cognitive status in a population of 250 individuals.

RESULTS

Mild to severe cognitive impairment was identifiable with a test accuracy of 70%. By enforcing a minimal eye tracking calibration error of 2 cm, we achieved an accuracy of 76% which is equivalent to the accuracy obtained using commercial hardware for eye-tracking.

CONCLUSION

This work demonstrates a mobile version of VisMET capable of estimating the presence of cognitive impairment.

SIGNIFICANCE

Given the ubiquity of tablet devices, our approach has the potential to scale globally.

The intersection between the oculomotor and hippocampal memory systems: empirical developments and clinical implications

Decades of cognitive neuroscience research has shown that where we look is intimately connected to what we remember. In this article, we review findings from human and nonhuman animals, using behavioral, neuropsychological, neuroimaging, and computational modeling methods, to show that the oculomotor and hippocampal memory systems interact in a reciprocal manner, on a moment-to-moment basis, mediated by a vast structural and functional network. Visual exploration serves to efficiently gather information from the environment for the purpose of creating new memories, updating existing memories, and reconstructing the rich, vivid details from memory. Conversely, memory increases the efficiency of visual exploration. We call for models of oculomotor control to consider the influence of the hippocampal memory system on the cognitive control of eye movements, and for models of hippocampal and broader medial temporal lobe function to consider the influence of the oculomotor system on the development and expression of memory. We describe eye movement-based applications for the detection of neurodegeneration and delivery of therapeutic interventions for mental health disorders for which the hippocampus is implicated and memory dysfunctions are at the forefront.

VisMET: a passive, efficient, and sensitive assessment of visuospatial memory in healthy aging, mild cognitive impairment, and Alzheimer's disease

The entorhinal-hippocampal circuit is one of the earliest sites of cortical pathology in Alzheimer's disease (AD). Visuospatial memory paradigms that are mediated by the entorhinal-hippocampal circuit may offer a means to detect memory impairment during the early stages of AD. In this study, we developed a 4-min visuospatial memory paradigm called VisMET (Visuospatial Memory Eye-Tracking Task) that passively assesses memory using eye movements rather than explicit memory judgements. We had 296 control or memory-impaired participants view a set of images followed by a modified version of the images with either an object removed, or a new object added. Healthy controls spent significantly more time viewing these manipulations compared to subjects with mild cognitive impairment and AD. Using a logistic regression model, the amount of time that individuals viewed these manipulations could predict cognitive impairment and disease status with an out of sample area under the receiver-operator characteristic curve of 0.85. Based on these results, VisMET offers a passive, sensitive, and efficient memory paradigm capable of detecting objective memory impairment and predicting cognitive and disease status.