The human retina contains five photoreceptor types: rods; short (S)-, mid (M)-, and long (L)-wavelength–sensitive cones; and melanopsin-expressing ganglion cells. Recently, it has been shown that selective increments in M-cone activation are paradoxically perceived as brightness decrements, as opposed to L-cone increments. Here we show that similar effects are also observed in the pupillary light response, whereby M-cone or S-cone increments lead to pupil dilation whereas L-cone or melanopic illuminance increments resulted in pupil constriction. Additionally, intermittent photoreceptor activation increased pupil constriction over a 30-min interval. Modulation of L-cone or melanopic illuminance within the 0.25–4-Hz frequency range resulted in more sustained pupillary constriction than light of constant intensity. Opposite results were found for S-cone and M-cone modulations (2 Hz), mirroring the dichotomy observed in the transient responses. The transient and sustained pupillary light responses therefore suggest that S- and M-cones provide inhibitory input to the pupillary control system when selectively activated, whereas L-cones and melanopsin response fulfill an excitatory role. These findings provide insight into functional networks in the human retina and the effect of color-coding in nonvisual responses to light, and imply that nonvisual and visual brightness discrimination may share a common pathway that starts in the retina.

Study objectivesTo determine the effect of light exposure on subsequent sleep characteristics under ambulatory field conditions.MethodsTwenty healthy participants were fitted with ambulatory polysomnography (PSG) and wrist-actigraphs to assess light exposure, rest–activity, sleep quality, timing, and architecture. Laboratory salivary dim-light melatonin onset was analyzed to determine endogenous circadian phase.ResultsLater circadian clock phase was associated with lower intensity (R2 = 0.34, χ2(1) = 7.19, p < .01), later light exposure (quadratic, controlling for daylength, R2 = 0.47, χ2(3) = 32.38, p < .0001), and to later sleep timing (R2 = 0.71, χ2(1) = 20.39, p < .0001). Those with later first exposure to more than 10 lux of light had more awakenings during subsequent sleep (controlled for daylength, R2 = 0.36, χ2(2) = 8.66, p < .05). Those with later light exposure subsequently had a shorter latency to first rapid eye movement (REM) sleep episode (R2 = 0.21, χ2(1) = 5.77, p < .05). Those with less light exposure subsequently had a higher percentage of REM sleep (R2 = 0.43, χ2(2) = 13.90, p < .001) in a clock phase modulated manner. Slow-wave sleep accumulation was observed to be larger after preceding exposure to high maximal intensity and early first light exposure (p < .05).ConclusionsThe quality and architecture of sleep is associated with preceding light exposure. We propose that light exposure timing and intensity do not only modulate circadian-driven aspects of sleep but also homeostatic sleep pressure. These novel ambulatory PSG findings are the first to highlight the direct relationship between light and subsequent sleep, combining knowledge of homeostatic and circadian regulation of sleep by light. Upon confirmation by interventional studies, this hypothesis could change current understanding of sleep regulation and its relationship to prior light exposure.Clinical trial detailsThis study was not a clinical trial. The study was ethically approved and nationally registered (NL48468.042.14).

Light is the most potent time cue that synchronizes (entrains) the circadian pacemaker to the 24-h solar cycle. This entrainment process is an interplay between an individual’s daily light perception and intrinsic pacemaker period under free-running conditions. Establishing individual estimates of circadian phase and period can be time-consuming. We show that circadian phase can be accurately predicted (SD = 1.1 h for dim light melatonin onset, DLMO) using 9 days of ambulatory light and activity data as an input to Kronauer’s limit-cycle model for the human circadian system. This approach also yields an estimated circadian period of 24.2 h (SD = 0.2 h), with longer periods resulting in later DLMOs. A larger amount of daylight exposure resulted in an earlier DLMO. Individuals with a long circadian period also showed shorter intervals between DLMO and sleep timing. When a field-based estimation of tau can be validated under laboratory studies in a wide variety of individuals, the proposed methods may prove to be essential tools for individualized chronotherapy and light treatment for shift work and jetlag applications. These methods may improve our understanding of fundamental properties of human circadian rhythms under daily living conditions.

This investigation is of clinical importance, because major confounding variables were excluded. The lack of comorbidities might be responsible for the absence of sundown syndrome and sleep disturbances commonly reported in AD patients. Whether there is interdependence between progressive decline in cognition and long sleep duration remains elusive. Future studies should address whether prolonged sleep at night and decreased day-time activity can be altered to delay the progression of cognitive decline.

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, SomnivoreTM, for automated wake–sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1–2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies.