Spontaneous attentional failures reflect multiplicative interactions of chronic sleep loss with acute sleep loss and circadian misalignment.

OBJECTIVES: Acute and chronic sleep loss and circadian timing interact such that, depending on their combination, small or very large performance decrements are observed in tasks of attention. Here, we tested whether such nonlinear interactions extend to a physiological measure of spontaneous visual attentional failures, indicating a fundamental principle of sleep-wake regulation. METHODS: Nine healthy volunteers completed an in-laboratory 3-week forced desynchrony protocol consisting of 12 consecutive 42.85-hour cycles with a sleep-wake ratio of 1:3.3. The protocol induced increasing chronic sleep loss, while extended wake (32.85 hours) and sleep episodes (10 hours) occurred at multiple circadian phases. Attentional failure rate was quantified from continuous electrooculograms (number of 30-second epochs with slow eye movements/h of wakefulness) as a function of time since scheduled wake (acute sleep loss), week of study (chronic sleep loss), and circadian (melatonin) phase. RESULTS: During the first ∼8 hours awake, attentional failure rate was low, irrespective of the week. During the following wake hours, attentional failure rate increased steadily but at a faster rate in weeks 2 and 3 compared to week 1. The effects of acute and chronic sleep loss on attentional failure rate were magnified during the biological night compared to the biological day. CONCLUSIONS: A single extended sleep episode can only temporarily reverse attentional impairment associated with chronic sleep loss. Multiplicative effects of acute and chronic sleep loss-further amplified during the biological night-substantiate the interaction of 2 homeostatic response mechanisms and caution against underestimating their disproportionate combined impact on performance, health, and safety.

Detecting slow eye movements using multi-scale one-dimensional convolutional neural network for driver sleepiness detection.

BACKGROUND: Slow eye movements (SEMs), which occurs during eye-closed periods with high time coverage rate during simulated driving process, indicate drivers' sleep onset. NEW METHOD: For the multi-scale characteristics of slow eye movement waveforms, we propose a multi-scale one-dimensional convolutional neural network (MS-1D-CNN) for classification. The MS-1D-CNN performs multiple down-sampling processing branches on the original signal and uses the local convolutional layer to extract the features for each branch. RESULTS: We evaluate the classification performance of this model on ten subjects' standard train-test datasets and continuous test datasets by means of subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the standard train-test datasets, the overall average classification accuracies are about 99.1% and 98.6%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the continuous test datasets, the overall average values of accuracy, precision, recall and F1-score are 99.3%, 98.9%, 99.5% and 99.1% in subject-subject evaluation, are 99.2%, 98.8%, 99.3% and 99.0% in leave-one-subject-out cross validation. COMPARISON WITH EXISTING METHOD: Results of the standard train-test datasets show that the overall average classification accuracy of the MS-1D-CNN is quite higher than the baseline method based on hand-designed features by 3.5% and 3.5%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. CONCLUSIONS: These results suggest that multi-scale transformation in the MS-1D-CNN model can enhance the representation ability of features, thereby improving classification accuracy. Experimental results verify the good performance of the MS-1D-CNN model, even in leave-one-subject-out cross validation, thus promoting the application of SEMs detection technology for driver sleepiness detection.

Elements of exogenous attentional cueing preserved during optokinetic motion of the visual scene.

Navigating through our environment raises challenges for perception by generating salient background visual motion and eliciting prominent eye movements to stabilise the retinal image. It remains unclear if exogenous spatial attentional orienting is possible during background motion and the eye movements it causes and whether this compromises the underlying neural processing. To test this, we combined exogenous orienting, visual scene motion, and electroencephalography (EEG). A total of 26 participants viewed a background of moving black and grey bars (optokinetic stimulation). We tested for effects of non-spatially predictive peripheral cueing on visual motion discrimination of a target dot, presented either at the same (valid) or opposite (invalid) location as the preceding cue. Valid cueing decreased reaction times not only when participants kept their gaze fixed on a central point (fixation blocks) but also even when there was no fixation point, so that participants performed intensive, repetitive tracking eye movements (eye movement blocks). Overall, manual response reaction times were slower during eye movements. Cueing also produced reliable effects on neural activity on either block, including within the first 120 ms of neural processing of the target. The key pattern with larger event-related potential (ERP) amplitudes on invalid versus valid trials showed that the neural substrate of exogenous cueing was highly similar during eye movements or fixation. Exogenous peripheral cueing and its neural correlates are robust against distraction from the moving visual scene, important for perceptual cognition during navigation.

Right frontal eye field has perceptual and oculomotor functions during optokinetic stimulation and nystagmus.

The right frontal eye field (rFEF) is associated with visual perception and eye movements. rFEF is activated during optokinetic nystagmus (OKN), a reflex that moves the eye in response to visual motion (optokinetic stimulation, OKS). It remains unclear whether rFEF plays causal perceptual and/or oculomotor roles during OKS and OKN. To test this, participants viewed a leftward-moving visual scene of vertical bars and judged whether a flashed dot was moving. Single pulses of transcranial magnetic stimulation (TMS) were applied to rFEF on half of trials. In half of blocks, to explore oculomotor control, participants performed an OKN in response to the OKS. rFEF TMS, during OKN, made participants more accurate on trials when the dot was still, and it slowed eye movements. In separate blocks, participants fixated during OKS. This not only controlled for eye movements but also allowed the use of EEG to explore the FEF's role in visual motion discrimination. In these blocks, by contrast, leftward dot motion discrimination was impaired, associated with a disruption of the frontal-posterior balance in alpha-band oscillations. None of these effects occurred in a control site (M1) experiment. These results demonstrate multiple related yet dissociable causal roles of the right FEF during optokinetic stimulation.NEW & NOTEWORTHY This study demonstrates causal roles of the right frontal eye field (FEF) in motion discrimination and eye movement control during visual scene motion: previous work had only examined other stimuli and eye movements such as saccades. Using combined transcranial magnetic stimulation and EEG and a novel optokinetic stimulation motion-discrimination task, we find evidence for multiple related yet dissociable causal roles within the FEF: perceptual processing during optokinetic stimulation, generation of the optokinetic nystagmus, and the maintenance of alpha oscillations.

How Eye Movements Stabilize Posture in Patients With Bilateral Vestibular Hypofunction.

Chronic patients with bilateral vestibular hypofunction (BVH) complain of oscillopsia and great instability particularly when vision is excluded and on irregular surfaces. The real nature of the visual input substituting to the missing vestibular afferents and improving posture control remains however under debate. Is retinal slip involved? Do eye movements play a substantial role? The present study tends to answer this question in BVH patients by investigating their posture stability during quiet standing in four different visual conditions: total darkness, fixation of a stable space-fixed target, and pursuit of a visual target under goggles delivering visual input rate at flicker frequency inducing either slow eye movements (4.5 Hz) or saccades (1.2 Hz). Twenty one chronic BVH patients attested by both the caloric and head impulse test were examined by means of static posturography, and compared to a control group made of 21 sex-and age-matched healthy participants. The posturography data were analyzed using non-linear computation of the center of foot pressure (CoP) by means of the wavelet transform (Power Spectral Density in the visual frequency part, Postural Instability Index) and the fractional Brownian-motion analysis (stabilogram-diffusion analysis, Hausdorff fractal dimension). Results showed that posture stability was significantly deteriorated in darkness in the BVH patients compared to the healthy controls. Strong improvement of BVH patients' posture stability was observed during fixation of a visual target, pursuit with slow eye movements, and saccades, whereas the postural performance of the control group was less affected by the different visual conditions. It is concluded that BVH patients improve their posture stability by (1) using extraocular signals from eye movements (efference copy, muscle re-afferences) much more than the healthy participants, and (2) shifting more systematically than the controls to a more automatic mode of posture control when they are in dual-task conditions associating the postural task and a concomitant visuo- motor task.

An improved algorithm for the automatic detection and characterization of slow eye movements.

Slow eye movements (SEMs) are typical of drowsy wakefulness and light sleep. SEMs still lack of systematic physical characterization. We present a new algorithm, which substantially improves our previous one, for the automatic detection of SEMs from the electro-oculogram (EOG) and extraction of SEMs physical parameters. The algorithm utilizes discrete wavelet decomposition of the EOG to implement a Bayes classifier that identifies intervals of slow ocular activity; each slow activity interval is segmented into single SEMs via a template matching method. Parameters of amplitude, duration, velocity are automatically extracted from each detected SEM. The algorithm was trained and validated on sleep onsets and offsets of 20 EOG recordings visually inspected by an expert. Performances were assessed in terms of correctly identified slow activity epochs (sensitivity: 85.12%; specificity: 82.81%), correctly segmented single SEMs (89.08%), and time misalignment (0.49 s) between the automatically and visually identified SEMs. The algorithm proved reliable even in whole sleep (sensitivity: 83.40%; specificity: 72.08% in identifying slow activity epochs; correctly segmented SEMs: 93.24%; time misalignment: 0.49 s). The algorithm, being able to objectively characterize single SEMs, may be a valuable tool to improve knowledge of normal and pathological sleep.