Disentangling Visual Exploration Differences in Cognitive Impairment.

OBJECTIVE: Individuals with cognitive impairment (CI) exhibit different oculomotor functions and viewing behaviors. In this work we aimed to quantify the differences in these functions with CI severity, and assess general CI and specific cognitive functions related to visual exploration behaviors. METHODS: A validated passive viewing memory test with eyetracking was administered to 348 healthy controls and CI individuals. Spatiotemporal properties of the scanpath, the semantic category of the viewed regions, and other composite features were extracted from the estimated eyegaze locations on the corresponding pictures displayed during the test. These features were then used to characterize viewing patterns, classify cognitive impairment, and estimate scores in various neuropsychological tests using machine learning. RESULTS: Statistically significant differences in spatial, spatiotemporal, and semantic features were found between healthy controls and individuals with CI. The CI group spent more time gazing at the center of the image, looked at more regions of interest (ROI), transitioned less often between ROI yet in a more unpredictable manner, and exhibited different semantic preferences. A combination of these features achieved an area under the receiver-operator curve of 0.78 in differentiating CI individuals from controls. Statistically significant correlations were identified between actual and estimated CI scores and other neuropsychological tests. CONCLUSION: Evaluating visual exploration behaviors provided quantitative and systematic evidence of differences in CI individuals, leading to an improved approach for passive cognitive impairment screening. SIGNIFICANCE: The proposed passive, accessible, and scalable approach could help with earlier detection and a better understanding of cognitive impairment.

Disentangling visual exploration differences in cognitive impairment.

Objective: Compared to individuals without cognitive impairment (CI), those with CI exhibit differences in both basic oculomotor functions and complex viewing behaviors. However, the characteristics of the differences and how those differences relate to various cognitive functions have not been widely explored. In this work we aimed to quantify those differences and assess general cognitive impairment and specific cognitive functions. Methods: A validated passive viewing memory test with eyetracking was administered to 348 healthy controls and CI individuals. Spatial, temporal, semantic, and other composite features were extracted from the estimated eye-gaze locations on the corresponding pictures displayed during the test. These features were then used to characterize viewing patterns, classify cognitive impairment, and estimate scores in various neuropsychological tests using machine learning. Results: Statistically significant differences in spatial, spatiotemporal, and semantic features were found between healthy controls and individuals with CI. CI group spent more time gazing at the center of the image, looked at more regions of interest (ROI), transitioned less often between ROI yet in a more unpredictable manner, and had different semantic preferences. A combination of these features achieved an area under the receiver-operator curve of 0.78 in differentiating CI individuals from controls. Statistically significant correlations were identified between actual and estimated MoCA scores and other neuropsychological tests. Conclusion: Evaluating visual exploration behaviors provided quantitative and systematic evidence of differences in CI individuals, leading to an improved approach for passive cognitive impairment screening. Significance: The proposed passive, accessible, and scalable approach could help with earlier detection and a better understanding of cognitive impairment.

Automated analysis of facial emotions in subjects with cognitive impairment.

Differences in expressing facial emotions are broadly observed in people with cognitive impairment. However, these differences have been difficult to objectively quantify and systematically evaluate among people with cognitive impairment across disease etiologies and severity. Therefore, a computer vision-based deep learning model for facial emotion recognition trained on 400.000 faces was utilized to analyze facial emotions expressed during a passive viewing memory test. In addition, this study was conducted on a large number of individuals (n = 493), including healthy controls and individuals with cognitive impairment due to diverse underlying etiologies and across different disease stages. Diagnoses included subjective cognitive impairment, Mild Cognitive Impairment (MCI) due to AD, MCI due to other etiologies, dementia due to Alzheimer's diseases (AD), and dementia due to other etiologies (e.g., Vascular Dementia, Frontotemporal Dementia, Lewy Body Dementia, etc.). The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance across all participants. A participant with a score of less than or equal to 24 was considered cognitively impaired (CI). Compared to cognitively unimpaired (CU) participants, CI participants expressed significantly less positive emotions, more negative emotions, and higher facial expressiveness during the test. In addition, classification analysis revealed that facial emotions expressed during the test allowed effective differentiation of CI from CU participants, largely independent of sex, race, age, education level, mood, and eye movements (derived from an eye-tracking-based digital biomarker for cognitive impairment). No screening methods reliably differentiated the underlying etiology of the cognitive impairment. The findings provide quantitative and comprehensive evidence that the expression of facial emotions is significantly different in people with cognitive impairment, and suggests this may be a useful tool for passive screening of cognitive impairment.

A feasibility trial of gamma sensory flicker for patients with prodromal Alzheimer's disease.

INTRODUCTION: We and collaborators discovered that flickering lights and sound at gamma frequency (40 Hz) reduce Alzheimer's disease (AD) pathology and alter immune cells and signaling in mice. To determine the feasibility of this intervention in humans we tested the safety, tolerability, and daily adherence to extended audiovisual gamma flicker stimulation. METHODS: Ten patients with mild cognitive impairment due to underlying AD received 1-hour daily gamma flicker using audiovisual stimulation for 4 or 8 weeks at home with a delayed start design. RESULTS: Gamma flicker was safe, tolerable, and adherable. Participants' neural activity entrained to stimulation. Magnetic resonance imaging and cerebral spinal fluid proteomics show preliminary evidence that prolonged flicker affects neural networks and immune factors in the nervous system. DISCUSSION: These findings show that prolonged gamma sensory flicker is safe, tolerable, and feasible with preliminary indications of immune and network effects, supporting further study of gamma stimulation in AD.

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.

Informed Consent in Two Alzheimer's Disease Research Centers: Insights From Research Coordinators.

Background: Informed consent (IC) is critical to performing ethical research. Unfortunately, the IC process and supporting IC forms are frequently burdensome and do not necessarily meet the informational needs of participants. The intersecting legal and ethical challenges of obtaining IC from individuals with memory or cognitive deficits further exacerbate existing IC shortcomings. For this reason, study coordinators play a critical role in facilitating the IC process in Alzheimer's disease (AD) research. To identify opportunities to improve how IC is obtained in AD research, we examined the IC process from the perspectives of study coordinators at two Alzheimer's Disease Research Centers (ADRC). Methods: We performed semi-structured interviews with 15 study coordinators from two ADRC sites detailing their experience obtaining IC. Interviews were conducted in private, recorded, transcribed, and independently coded using the constant comparative method of grounded theory. Key themes were explored as they emerged. Results: Coordinators reported overall satisfaction with the IC process. However, many reported difficulties maintaining participant attention, explaining complex procedures, and addressing medical misinformation. Although the centers use site-specific consent forms, coordinators at both centers stressed that their IC is too long and the supporting IC forms are too complicated. Coordinators indicated modifying the IC process to the perceived needs of individual participants. Adaptations reported include altering the cadence and vocabulary they employ, using supplemental materials, varying the order of IC topics, and limiting the depth of information presented. Conclusion: A qualitative analysis of interviews with study coordinators reveals opportunities to improve how we obtain IC in AD research. These insights will be used to create an electronic informed consent (eConsent) designed to boost engagement, enhance trust, and improve understanding by supporting participants' direct agency in the IC process.

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.

Eye tracking during a visual paired comparison task as a predictor of early dementia.

The authors present findings from a behavioral task (visual paired comparison) using infrared eye-tracking that could potentially be useful in predicting the onset of Alzheimer's disease. Delay intervals of 2 seconds and 2 minutes were used between the initial viewing of a picture and when the picture was displayed alongside a novel picture. Eye-tracking revealed that at the 2-second delay, 6 patients with mild cognitive impairment, 15 matched control participants (normal control), and 4 neurological control participants with Parkinson's disease performed comparably, viewing the novel picture greater than 71% of the time. When the delay increased to 2 minutes, patients with mild cognitive impairment viewed the novel picture only 53% of the time (P < .05), while control participants and participants with Parkinson's disease remained above 70%. These findings demonstrate the usefulness of this task for assessing normal as well as impaired memory function.