The authors present profiles of performance on 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 sec and 2 min 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 mild cognitively impaired patients (MCI), 15 matched control subjects (NC), and 4 neurological control subject's with Parkinson's Disease (PD) performed comparably, i.e., viewed the novel picture greater than 71% of the time. When the delay increased to 2 minutes, MCI patients viewed the novel picture only 53% of the time (p < .05), while NC and PD remained above 70%. These findings are consistent with the idea that the MCI patients did not remember well which picture was recently viewed. These findings demonstrate the usefulness of this task for assessing normal as well as impaired memory function.

The Visual Paired Comparison (VPC) task is a recognition memory test that has shown promise for the detection of memory impairments associated with mild cognitive impairment (MCI). Because patients with MCI often progress to Alzheimer’s Disease (AD), the VPC may be useful in predicting the onset of AD. VPC uses noninvasive eye tracking to identify how subjects view novel and repeated visual stimuli. Healthy control subjects demonstrate memory for the repeated stimuli by spending more time looking at the novel images, i.e., novelty preference. Here, we report an application of machine learning methods from computer science to improve the accuracy of detecting MCI by modeling eye movement characteristics such as fixations, saccades, and re-fixations during the VPC task. These characteristics are represented as features provided to automatic classification algorithms such as Support Vector Machines (SVMs). Using the SVM classification algorithm, in tandem with modeling the patterns of fixations, saccade orientation, and regression patterns, our algorithm was able to automatically distinguish age-matched normal control subjects from MCI subjects with 87% accuracy, 97% sensitivity and 77% specificity, compared to the best available classification performance of 67% accuracy, 60% sensitivity, and 73% specificity when using only the novelty preference information. These results demonstrate the effectiveness of applying machine-learning techniques to the detection of MCI, and suggest a promising approach for detection of cognitive impairments associated with other disorders.

Background/Rationale Currently, we cannot reliably differentiate individuals at risk of cognitive decline, e.g., Mild Cognitive Impairment (MCI), Alzheimer’s disease (AD) from those individuals who are not at risk. Methods Thirty-two subjects with MCI and 60 control (CON) subjects were tested on an innovative, sensitive behavioral assay, the Visual Paired Comparison (VPC) task using infrared eyetracking. Subjects were followed for three years after testing. Results Scores on the VPC task predicted, up to three years prior to a change in clinical diagnosis, those MCI patients who would and those who would not progress to AD, and CON subjects who would and would not progress to MCI. Conclusions The present findings show that the VPC task can predict impending cognitive decline. To our knowledge, this is the first behavioral task that can identify CON subjects who will develop MCI or MCI subjects who will develop AD within the next few years.

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.

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