Pupil Size Variability Research Articles

Individual differences in baseline eye movement indices: Examining the relationships between baseline pupil size, inhibitory control, and fixation stability.

The relationship among baseline pupil size, fixation stability, and inhibitory control were examined in this study. Participants performed a baseline eye measure in which they were instructed to stare at a fixation dot on screen for 2 min. Following the baseline eye measure, participants completed an antisaccade task to measure inhibitory control ability. We found a correlation between baseline pupil size variability and inhibitory control, as well as between fixation stability and inhibitory control. We showed that participants with better inhibitory control exhibited larger variability in pupil size, and those with better fixation stability showed superior inhibitory control ability. Overall, our results indicate that there are significant correlations between inhibitory control and baseline pupil size, as well as between inhibitory control and fixation stability.

Effects of recent cannabis consumption on eye-tracking and pupillometry.

Cannabis consumption is known to immediately affect ocular and oculomotor function, however, cannabis consumption is also known to affect it for a prolonged period of time. The purpose of this study is to identify an eye tracking or pupillometry metric which is affected after recent cannabis consumption but is not confounded by cannabis consumption history or demographic variables. Quasi-experimental design. Participants who would consume inhalable cannabis (n = 159, mean age 31.0 years, 54% male) performed baseline neurobehavioral testing and eye-function assessments when they were sober. Eye function assessments included eye-tracking [gaze (point of visual focus), saccades (smooth movement)] and pupillometry. Participants then inhaled cannabis until they self-reported to be high and performed the same assessment again. Controls who were cannabis naïve or infrequent users (n = 30, mean age 32.6 years, 57% male) performed the same assessments without consuming cannabis in between. Cannabis significantly affected several metrics of pupil dynamics and gaze. Pupil size variability was the most discriminant variable after cannabis consumption. This variable did not change in controls on repeat assessment (i.e., no learning effect), did not correlate with age, gender, race/ethnicity, or self-reported level of euphoria, but did correlate with THC concentration of cannabis inhaled. A novel eye-tracking metric was identified that is affected by recent cannabis consumption and is not different from non-users at baseline. A future study that assesses pupil size variability at multiple intervals over several hours and quantifies cannabis metabolites in biofluids should be performed to identify when this variable normalizes after consumption and if it correlates with blood THC levels.

Gaze and pupil size variability predict difficulty-level and safe intersection crosses in a driving simulator

Western populations are ageing. With age comes an increased risk of mild cognitive impairment (MCI) and fragility that leads to higher fatal car crashes. This study develops a driving simulation paradigm that seeks to detect unsafe drivers, particularly among older drivers with MCI. The paradigm includes repeated urban intersection crossings at three difficulty levels while eye movements are tracked. The internal validity of this part of the paradigm was tested with young adults ( N = 7). Results indicated that the simulator tests elicited unsafe driving behaviors that varied across difficulty and avoided ceiling and floor effects. Eye movement metrics associated with cognitive load also varied with difficulty and predicted safe crosses. The strongest predictors were gaze transition entropy, gaze variability, and pupil size entropy. These findings indicate internal validity of the tests. Future research should test the external validity of this paradigm with a larger, more diverse sample.

Sympathetic and Parasympathetic Modulation of Pupillary Unrest.

Pupillary unrest is an established indicator of drowsiness or sleepiness. How sympathetic and parasympathetic activity contribute to pupillary unrest is not entirely unclear. In this study, we investigated 83 young healthy volunteers to assess the relationship of pupillary unrest to other markers of the autonomic nervous system. Sample entropy (SE) and the established pupillary unrest index (PUI) were calculated to characterize pupil size variability. Autonomic indices were derived from heart rate, blood pressure, respiration, and skin conductance. Additionally, we assessed individual levels of calmness, vigilance, and mood. In an independent sample of 26 healthy participants, we stimulated the cardiovagal system by a deep breathing test. PUI was related to parasympathetic cardiac indices and sleepiness. A linear combination of vagal heart rate variability [root mean square of heart beat interval differences (RMSSD)] and skin conductance fluctuations (SCFs) was suited best to explain interindividual variance of PUI. Complexity of pupil diameter (PD) variations correlated to indices of sympathetic skin conductance. Furthermore, we found that spontaneous fluctuations of skin conductance are accompanied by increases of pupil size. In an independent sample, we were able to corroborate the relation of PUI with RMSSD and skin conductance. A slow breathing test enhanced RMSSD and PUI proportionally to each other, while complexity of PD dynamics decreased. Our data suggest that the slow PD oscillations (f < 0.15 Hz) quantified by PUI are related to the parasympathetic modulation. Sympathetic arousal as detected by SCFs is associated to transient pupil size increases that increase non-linear pupillary dynamics.

Steady-State Pupil Size Varies with Circadian Phase and Sleep Homeostasis in Healthy Young Men.

Pupil size informs about sympathovagal balance as well as cognitive and affective processes, and perception. It is also directly linked to phasic activity of the brainstem locus coeruleus, so that pupil measures have gained recent attention. Steady-state pupil size and its variability have been directly linked to sleep homeostasis and circadian phase, but results have been inconsistent. Here, we report robust changes in steady-state pupil size during 29 h of continuous wakefulness in healthy young men (N = 20; 18–30 years old) maintained in dim-light in strictly controlled constant routine conditions. These variations were associated with variations in motivation and sustained attention performance. Pupil size variability did not significantly change during the protocol. Yet, pupil size variability was linearly associated with subjective fatigue, sociability, and anguish. No associations were found between neither steady-state pupil size nor pupil size variability, and objective EEG measure of alertness and subjective sleepiness. Our data support therefore the notion that, compared with its variability, steady-state pupil size is strongly influenced by the concomitant changes in sleep need and circadian phase. In addition, steady-state pupil size appears to be related to motivation and attention, while its variability may be related to separate affective dimensions and subjective fatigue.

Individual differences in resting-state pupil size: Evidence for association between working memory capacity and pupil size variability

Dynamic non-luminance-mediated changes in pupil diameter have frequently been shown to be a reliable index for the level of arousal, mental effort, and activity in the locus coeruleus, the brainstem's noradrenergic arousal center. While pupillometry has most commonly been used to assess the level of arousal in particular psychological states or the level of engagement in cognitive tasks, some recent studies have found a relationship between average resting-state (i.e. baseline) pupil sizes and individuals' working memory capacity (WMC), indicating that individuals with higher WMC on average have larger pupils than individuals with relatively lower WMC. In the present study, we measured pupil size continuously in 212 participants during rest (i.e. while fixating) and estimated WMC in all participants by administering the Letter-Number Sequencing (LNS) task from WAIS-III. We were unable to replicate the relation between average pupil size and WMC. However, the novel finding was that higher WMC was associated with higher variability in resting-state pupil size. The present results are relevant for the current debate on the role of noradrenergic activity on working memory capacity.

Forecasting transient sleep episodes by pupil size variability

Abstract The ability to predict when a person is about to fall asleep is an important challenge in recent biomedical research and has various possible applications. Sleepiness and fatigue are known to increase pupillary fluctuations and the occurrence of eye blinks. In this study, we evaluated the use of the pupil diameter to forecast sleep episodes of short duration (&gt;1s). We conducted multi-channel physiological and pupillometric recordings (diameter, gaze position) in 91 healthy volunteers at rest in supine position. Although they were instructed to keep their eyes open, short sleep episodes were detected in 20 participants (16 males, age: 26.2±5.6 years), 53 events in total. Before each sleep event, pupil size was extracted in a window of 30s (without additional sleep event). Mean pupil diameter and its standard deviation, Shannon entropy and wavelet entropy in the first half (15s) were compared to the second half of the window (15s). Linear and nonlinear measures demonstrated an elevation of pupil size variability before sleep onset. Most obviously, WE and SD increased significantly from 0.054±0.056 and 0.38±0.16 mm to 0.113±0.103 (T(102)=2.44, p&lt;0.001) and 0.46±0.18 mm (T(104)=3.67, p&lt;0.05) in the second half of each analysis window. We were able to identify 83% of the pre-sleep segments by linear discriminant analysis. Although our data was acquired in an experimental condition, it suggests that pupillary unrest might be a suitable predictor of events related to transient sleep or inattentiveness. In the future, we are going to involve the other recorded physiological signals into the analysis.

System and measurement method for binocular pupillometry to study pupil size variability.

BackgroundAn objective and noninvasive examination of pupil size variability can be used to assess the activity of the autonomous nervous system. We designed a system that enables binocular, fast, and accurate recordings of different types of pupil variabilities, which are synchronous with other biosignals. This type of measurement system is needed to extend the scope of pupillometry applications.MethodsIn the proposed system, the left and right eyes are independently and interchangeably illuminated to generate alternating images, which are successively acquired by a single camera. The system is composed of four functional modules: the image acquisition module, the image processing unit, the light stimulator, and the controller. The proposed image processing algorithm approximates the shape of the pupil using the best-fit ellipse. The user control panel (controller) precisely sets the stimuli parameters and controls the entire measurement procedure.ResultsThe computer-based binocular system records the pupil size during the pupil light reflexes (direct and indirect) and spontaneous pupil size fluctuations, at a sampling rate up to 75 Hz, with a resolution better than 0.02 mm. Our initial laboratory tests confirmed that the new system is fast and precise (system accuracy better than 0.5% and repeatability better than 4%).ConclusionsThe proposed system’s unique geometry and construction, and the method it uses to detect images from each eye, allows us to monitor the right and left eyes using a single camera with no overlap between the images. The system does not require a very experienced operator, because it is relatively simple and easy to use. Importantly, it is comfortable for the subjects. Additionally, the presented system can operate with other bio-measurement systems using a synchronous signal. These system capabilities can expand the scope of pupillometry research applications.

Posture change affects pupil size — Males in their twenties

To determine the effect of posture change on autonomic activity and to investigate valid parameters to reflex the autonomic activity from time-series pupil size data, a posture-related experiment was performed with 15 subjects, which involved measuring their electrocardiograms and pupil sizes. The experimental procedure consisted of three-sequence postures—supine, sitting and upright—for 5 min each, with rest sessions between postures. The subjects were notified of the entire experimental procedure. The parameters of the subjects' heart rate variability showed significant differences between the postures (heart rate: 63–70–80 beat, normalized low frequency power: 28–50–75, normalized high frequency power: 72–49–25 and ratio: 0.5–1.2–4.4 in supine–sitting–upright position respectively) as did the parameters of their mean pupil sizes (41,300–53,900–53,700 pixels respectively) and the major frequencies (the third trend: 0.23–0.2–0.18 Hz respectively) of their pupil size variability according to changes in their autonomic activities induced byposture change. The experiment thus proved that posture change affects autonomic activity and that such activity can be estimated by the parameters of pupil size as similar as heart rate variability.

Pupil size variability as an index of autonomic activity — A preliminary study

This paper sought to investigate pupil size variability, pupil size parameters in terms of time domain and frequency domain, the autonomic activity change induced by posture change, degree of sleepiness and cognitive task (math task). With a specially designed pupil image acquisition system in the dark room, these three kinds of experiments were performed to induce a dominant state of sympathetic or parasympathetic activation. Electrocardiogram and pupil size were measured in all the experiments.

Evaluating several sources of variability for standard and SWAP visual fields in glaucoma patients, suspects, and normals

Purpose To quantify factors affecting test-retest variability of threshold measurements over a series of 3 serial visual fields (VF). Design Prospective comparative observational study. Participants Forty-one normals, 10 suspects and 35 stable glaucoma patients. Methods All subjects performed 3 standard and 3 short-wavelength automated perimetry (SWAP) VFs. At each VF location, severity (defined as age-corrected total deviation) and test-retest variability (TRV), defined as the standard deviation of 3 serial threshold values, were calculated. A multiple regression model (constructed separately for standard VF and SWAP) incorporated 13 factors: severity, location, eccentricity, study group, diagnosis, superior versus inferior hemifield, nasal versus temporal hemifield, one-versus-two thresholds, age, mean pupil size, pupil size variability, between-subject variation, and residual variation. Main outcome measures Variability in threshold sensitivity VF values. Results Mean TRV (± standard deviation) for normal, suspect and glaucoma eyes, respectively, was: 1.28 ± 0.87, 1.53 ± 1.04 and 2.20 ± 1.79 dB for standard VF, and 1.87 ± 1.35, 1.86 ± 1.24 and 2.68 ± 1.85 dB for SWAP. The contribution of each factor to the model for standard VF and SWAP (SWAP in parentheses) were: severity 15.5% (6.9%); location 2.7% (4.1%); eccentricity 1.1% (0.64%); diagnosis 2.9% (5.9%); “superior versus inferior” hemifield 0.17% (1.7%); “nasal versus temporal” hemifield 0.06% (0.02%); one-versus-two thresholds 0.04% (0.16%); age 0.1% (0.06%); mean pupil size 0.59% (0.1%); pupil size variability 3.2% (2.8%); between-subject 8.0% (13.5%) and residual variation 61.0% (66.6%). Excluding between-subject and residual variation, the 11-factor model was able to account for less than one third of the variability seen in both standard VF and SWAP. Conclusions Severity of defect and between subject variation exerted the largest effect on TRV. However, even if all 11 factors could be adjusted for, it would reduce the magnitude of TRV by only 30%. More work is needed to reduce the remaining variability inherent in psychophysical testing and to better understand the intrinsic physiological variability present both in healthy and diseased eyes. It is possible that a larger number of VFs used for the calculation of TRV might further reduce the magnitude of the remaining variability found in this study.