App-Based Saccade Latency and Directional Error Determination Across the Adult Age Spectrum.

OBJECTIVE: We aid in neurocognitive monitoring outside the hospital environment by enabling app-based measurements of visual reaction time (saccade latency) and directional error rate in a cohort of subjects spanning the adult age spectrum. METHODS: We developed an iOS app to record subjects with the frontal camera during pro- and anti-saccade tasks. We further developed automated algorithms for measuring saccade latency and directional error rate that take into account the possibility that it might not always be possible to determine the eye movement from app-based recordings. RESULTS: To measure saccade latency on a tablet, we ensured that the absolute timing error between on-screen task presentation and the camera recording is within 5 ms. We collected over 235,000 eye movements in 80 subjects ranging in age from 20 to 92 years, with 96% of recorded eye movements either declared good or directional errors. Our error detection code achieved a sensitivity of 0.97 and a specificity of 0.97. Confirming prior reports, we observed a positive correlation between saccade latency and age while the relationship between directional error rate and age was not significant. Finally, we observed significant intra- and inter-subject variations in saccade latency and directional error rate distributions, which highlights the importance of individualized tracking of these visual digital biomarkers. CONCLUSION AND SIGNIFICANCE: Our system and algorithms allow ubiquitous tracking of saccade latency and directional error rate, which opens up the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.

Freely scalable and reconfigurable optical hardware for deep learning.

As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of [Formula: see text]m.

Measuring Saccade Latency Using Smartphone Cameras.

OBJECTIVE: Accurate quantification of neurodegenerative disease progression is an ongoing challenge that complicates efforts to understand and treat these conditions. Clinical studies have shown that eye movement features may serve as objective biomarkers to support diagnosis and tracking of disease progression. Here, we demonstrate that saccade latency-an eye movement measure of reaction time-can be measured robustly outside of the clinical environment with a smartphone camera. METHODS: To enable tracking of saccade latency in large cohorts of patients and control subjects, we combined a deep convolutional neural network for gaze estimation with a model-based approach for saccade onset determination that provides automated signal-quality quantification and artifact rejection. RESULTS: Simultaneous recordings with a smartphone and a high-speed camera resulted in negligible differences in saccade latency distributions. Furthermore, we demonstrated that the constraint of chinrest support can be removed when recording healthy subjects. Repeat smartphone-based measurements of saccade latency in 11 self-reported healthy subjects resulted in an intraclass correlation coefficient of 0.76, showing our approach has good to excellent test-retest reliability. Additionally, we conducted more than 19 000 saccade latency measurements in 29 self-reported healthy subjects and observed significant intra- and inter-subject variability, which highlights the importance of individualized tracking. Lastly, we showed that with around 65 measurements we can estimate mean saccade latency to within less-than-10-ms precision, which takes within 4 min with our setup. CONCLUSION AND SIGNIFICANCE: By enabling repeat measurements of saccade latency and its distribution in individual subjects, our framework opens the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.

Determination of Saccade Latency Distributions using Video Recordings from Consumer-grade Devices.

Quantitative and accurate tracking of neurocognitive decline remains an ongoing challenge. We seek to address this need by focusing on robust and unobtrusive measurement of saccade latency - the time between the presentation of a visual stimulus and the initiation of an eye movement towards the stimulus - which has been shown to be altered in patients with neurocognitive decline or neurodegenerative diseases. Here, we present a novel, deep convolutional-neuralnetwork-based method to measure saccade latency outside of the clinical environment using a smartphone camera without the need for supplemental or special-purpose illumination. We also describe a model-based approach to estimate saccade latency that is less sensitive to noise compared to conventional methods. With this flexible and robust system, we collected over 11,000 saccade-latency measurements from 21 healthy individuals and found distinctive saccade-latency distributions across subjects. When analyzing intra-subject variability across time, we observed noticeable variations in the mean saccade latency and associated standard deviation. We also observed a potential learning effect that should be further characterized and potentially accounted for when interpreting saccade latency measurements.