Person:

Klerman, Elizabeth

We present a novel approach for analyzing biological time-series data using a context-free language (CFL) representation that allows the extraction and quantification of important features from the time-series. This representation results in Hierarchically AdaPtive (HAP) analysis, a suite of multiple complementary techniques that enable rapid analysis of data and does not require the user to set parameters. HAP analysis generates hierarchically organized parameter distributions that allow multi-scale components of the time-series to be quantified and includes a data analysis pipeline that applies recursive analyses to generate hierarchically organized results that extend traditional outcome measures such as pharmacokinetics and inter-pulse interval. Pulsicons, a novel text-based time-series representation also derived from the CFL approach, are introduced as an objective qualitative comparison nomenclature. We apply HAP to the analysis of 24 hours of frequently sampled pulsatile cortisol hormone data, which has known analysis challenges, from 14 healthy women. HAP analysis generated results in seconds and produced dozens of figures for each participant. The results quantify the observed qualitative features of cortisol data as a series of pulse clusters, each consisting of one or more embedded pulses, and identify two ultradian phenotypes in this dataset. HAP analysis is designed to be robust to individual differences and to missing data and may be applied to other pulsatile hormones. Future work can extend HAP analysis to other time-series data types, including oscillatory and other periodic physiological signals.

Sleep restriction causes impaired cognitive performance that can result in adverse consequences in many occupational settings. Individuals may rely on self-perceived alertness to decide if they are able to adequately perform a task. It is therefore important to determine the relationship between an individual’s self-assessed alertness and their objective performance, and how this relationship depends on circadian phase, hours since awakening, and cumulative lost hours of sleep. Healthy young adults (aged 18–34) completed an inpatient schedule that included forced desynchrony of sleep/wake and circadian rhythms with twelve 42.85-hour “days” and either a 1:2 (n = 8) or 1:3.3 (n = 9) ratio of sleep-opportunity:enforced-wakefulness. We investigated whether subjective alertness (visual analog scale), circadian phase (melatonin), hours since awakening, and cumulative sleep loss could predict objective performance on the Psychomotor Vigilance Task (PVT), an Addition/Calculation Test (ADD) and the Digit Symbol Substitution Test (DSST). Mathematical models that allowed nonlinear interactions between explanatory variables were evaluated using the Akaike Information Criterion (AIC). Subjective alertness was the single best predictor of PVT, ADD, and DSST performance. Subjective alertness alone, however, was not an accurate predictor of PVT performance. The best AIC scores for PVT and DSST were achieved when all explanatory variables were included in the model. The best AIC score for ADD was achieved with circadian phase and subjective alertness variables. We conclude that subjective alertness alone is a weak predictor of objective vigilant or cognitive performance. Predictions can, however, be improved by knowing an individual’s circadian phase, current wake duration, and cumulative sleep loss.

Sleep loss causes profound cognitive impairments and increases the concentrations of adenosine and adenosine A1 receptors in specific regions of the brain. Time courses for performance impairment and recovery differ between acute and chronic sleep loss, but the physiological basis for these time courses is unknown. Adenosine has been implicated in pathways that generate sleepiness and cognitive impairments, but existing mathematical models of sleep and cognitive performance do not explicitly include adenosine. Here, we developed a novel receptor-ligand model of the adenosine system to test the hypothesis that changes in both adenosine and A1 receptor concentrations can capture changes in cognitive performance during acute sleep deprivation (one prolonged wake episode), chronic sleep restriction (multiple nights with insufficient sleep), and subsequent recovery. Parameter values were estimated using biochemical data and reaction time performance on the psychomotor vigilance test (PVT). The model closely fit group-average PVT data during acute sleep deprivation, chronic sleep restriction, and recovery. We tested the model’s ability to reproduce timing and duration of sleep in a separate experiment where individuals were permitted to sleep for up to 14 hours per day for 28 days. The model accurately reproduced these data, and also correctly predicted the possible emergence of a split sleep pattern (two distinct sleep episodes) under these experimental conditions. Our findings provide a physiologically plausible explanation for observed changes in cognitive performance and sleep during sleep loss and recovery, as well as a new approach for predicting sleep and cognitive performance under planned schedules.