Distinct pattern of oculomotor impairment associated with acute sleep loss and circadian misalignment

Abstract

Key points: Inadequate sleep and irregular work schedules have not only adverse consequences for individual health and well-being, but also enormous economic and safety implications for society as a whole. This study demonstrates that visual motion processing and coordinated eye movements are significantly impaired when performed after sleep loss and during the biological night, and thus may be contributing to human error and accidents. Because affected individuals are often unaware of their sensorimotor and cognitive deficits, there is a critical need for non-invasive, objective indicators of mild, yet potentially unsafe, impairment due to disrupted sleep or biological rhythms. Our findings show that a set of eye-movement measures can be used to provide sensitive and reliable indicators of such mild neural impairments.

Abstract: Sleep loss and circadian misalignment have long been known to impair human cognitive and motor performance with significant societal and health consequences. It is well known that human reaction time to a visual cue is impaired following sleep loss and circadian misalignment, but it has remained unclear how more complex visuomotor control behaviour is altered under these conditions. In this study, we measured 14 parameters of the voluntary ocular tracking response of 12 human participants (six females) to systematically examine the effects of sleep loss and circadian misalignment using a constant routine 24-h acute sleep-deprivation paradigm. The combination of state-of-the-art oculometric and sleep-research methodologies allowed us to document, for the first time, large changes in many components of pursuit, saccades and visual motion processing as a function of time awake and circadian phase. Further, we observed a pattern of impairment across our set of oculometric measures that is qualitatively different from that observed previously with other mild neural impairments. We conclude that dynamic vision and visuomotor control exhibit a distinct pattern of impairment linked with time awake and circadian phase. Therefore, a sufficiently broad set of oculometric measures could provide a sensitive and specific behavioural biomarker of acute sleep loss and circadian misalignment. We foresee potential applications of such oculometric biomarkers assisting in the assessment of readiness-to-perform higher risk tasks and in the characterization of sub-clinical neural impairment in the face of a multiplicity of potential risk factors, including disrupted sleep and circadian rhythms.

Keywords: Fatigue; Human Performance; Sensorimotor Control.

Figure 1. Example data from one participant

Plot of the repeated measures for two of the oculometrics, latency and initial acceleration, for one participant. The vertical dashed line separates the daytime (leftward symbols) and night‐time (rightward symbols). Note that the temporal trends are well‐behaved (i.e. not obscured by noise, even at the individual level).

Figure 2. Effect of time awake on pursuit behaviour

The four panels plot mean oculometric measures (±SD across participants) of pursuit latency (A), initial pursuit acceleration (B), steady‐state pursuit gain (C), and proportion of the tracking response that is smooth (as opposed to saccadic) (D) as a function of time awake over a 24‐h cycle (3 daytime and 8 night‐time measures). Night‐time points showing significant impairment with respect to the daytime baseline (P < 0.05, post hoc 1‐tailed t test, Bonferroni–Holm corrected for repeated measures) are shown in red.

Figure 3. Effect of time awake on saccade behaviour

The four panels plot mean oculometric measures (±SD across participants) of saccadic rate (A), saccadic amplitude (B), peak saccadic velocity (slope) (C), and peak saccadic velocity (intercept) (D) as a function of time awake over a 24‐h cycle (3 daytime and 8 night‐time measures). Night‐time points showing significant impairment with respect to the daytime baseline (P < 0.05, 1‐tailed t test, Bonferroni–Holm corrected for repeated measures) are shown in red.

Figure 4. Effect of time awake on visual motion processing

The four panels plot mean oculometric measures (±SD across participants) of direction noise (A), speed noise (B), anisotropy (oblique effect) (C), and horizontal–vertical asymmetry (D) as a function of time awake over a 24 h cycle (3 daytime and 8 night‐time measures). Night‐time points showing significant impairment with respect to the daytime baseline (P < 0.05, 1‐tailed t test, Bonferroni–Holm corrected for repeated measures) are shown in red.

Figure 5. Receiver operating characteristic (ROC) analysis at the nadir of performance

The panels plot the histogram of the night‐time run no. 6 measures from 12 participants, for each of the 14 different oculometrics, and their best fitting Gaussian (blue), along with best‐fitting Gaussian to the 12 baseline measures (red). The area under the curve (AUC) indicates the accuracy of a two‐alternative forced choice in distinguishing between randomly selected samples from the affected and baseline distributions. Although Gaussian fits are provided for visual insight, the ROC test is non‐parametric, acting directly on the measured values.

Figure 6. Effect of circadian phase on pursuit behaviour

The four panels plot mean oculometric measures (±SD across participants) of pursuit latency (A), initial pursuit acceleration (B), steady‐state pursuit gain (C), and proportion smooth (D) as a function of circadian phase over a 24 h cycle along with the best‐fitting sinusoid. The mean modulation of melatonin (blue) and cortisol (red) are superimposed here and in Figs 7 and 8. The SD of acrophase across participants was 13.9 deg. Note expanded scale for panel A. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 7. Effect of circadian phase on saccade behaviour

The four panels plot mean oculometric measures (±SD across participants) of saccadic rate (A), saccadic amplitude (B), peak saccadic velocity (slope) (C), and peak saccadic velocity (intercept) (D) as a function of circadian phase over a 24 h cycle along with the best‐fitting sinusoid. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 8. Effect of circadian phase on visual motion processing

The four panels plot mean oculometric measures (±SD across participants) of direction noise (A), speed noise (B), anisotropy (oblique effect) (C), and horizontal–vertical asymmetry (D) as a function of circadian phase over a 24 h cycle along with the best‐fitting sinusoid. [Color figure can be viewed at http://wileyonlinelibrary.com]