Time management in a co-housed social rodent species (Arvicanthis niloticus)

Sociality has beneficial effects on fitness, and timing the activities of animals may be critical. Social cues could influence daily rhythmic activities via direct effects on the circadian clock or on processes that bypass it (masking), but these possibilities remain incompletely addressed. We investigated the effects of social cues on the circadian body temperature (Tb) rhythms in pairs of co-housed and isolated grass rats, Arvicanthis niloticus (a social species), in constant darkness (DD). Cohabitation did not induce synchronization of circadian Tb rhythms. However, socio-sexual history did affect circadian properties: accelerating the clock in sexually experienced males and females in DD and advancing rhythm phase in the females in a light-dark cycle. To address whether synchronization occurs at an ultradian scale, we analyzed Tb and activity rhythms in pairs of co-housed sisters or couples in DD. Regardless of pair type, co-housing doubled the percentage of time individuals were simultaneously active without increasing individual activity levels, suggesting that activity bouts were synchronized by redistribution over 24 h. Together, our laboratory findings show that social cues affect individual time allocation budgets via mechanisms at multiple levels of biological organization. We speculate that in natural settings these effects could be adaptive, especially for group-living animals

Objective Measurement of Physician Stress in the Emergency Department Using a Wearable Sensor

Physician stress, and resultant consequences such as burnout, have become increasingly recognized pervasive problems, particularly within the specialty of Emergency Medicine. Stress is difficult to measure objectively, and research predominantly relies on self-reported measures. The present study aims to characterize digital biomarkers of stress as detected by a wearable sensor among Emergency Medicine physicians. Physiologic data was continuously collected using a wearable sensor during clinical work in the emergency department, and participants were asked to self-identify episodes of stress. Machine learning algorithms were used to classify self-reported episodes of stress. Comparing baseline sensor data to data in the 20-minute period preceding self-reported stress episodes demonstrated the highest prediction accuracy for stress. With further study, detection of stress via wearable sensors could be used to facilitate evidence-based stress research and just-in-time interventions for emergency physicians and other high-stress professionals

Towards Device Agnostic Detection of Stress and Craving in Patients with Substance Use Disorder

Novel technologies have great potential to improve the treatment of individuals with substance use disorder (SUD) and to reduce the current high rate of relapse (i.e. return to drug use). Wearable sensor-based systems that continuously measure physiology can provide information about behavior and opportunities for real-time interventions. We have previously developed an mHealth system which includes a wearable sensor, a mobile phone app, and a cloud-based server with embedded machine learning algorithms which detect stress and craving. The system functions as a just-in-time intervention tool to help patients de-escalate and as a tool for clinicians to tailor treatment based on stress and craving patterns observed. However, in our pilot work we found that to deploy the system to diverse socioeconomic populations and to increase usability, the system must be able to work efficiently with cost-effective and popular commercial wearable devices. To make the system device agnostic, methods to transform the data from a commercially available wearable for use in algorithms developed from research grade wearable sensor are proposed. The accuracy of these transformations in detecting stress and craving in individuals with SUD is further explored

Comparison of oxygen supplementation in very preterm infants: Variations of oxygen saturation features and their application to hypoxemic episode based risk stratification

BackgroundOxygen supplementation is commonly used to maintain oxygen saturation (SpO2) levels in preterm infants within target ranges to reduce intermittent hypoxemic (IH) events, which are associated with short- and long-term morbidities. There is not much information available about differences in oxygenation patterns in infants undergoing such supplementations nor their relation to observed IH events. This study aimed to describe oxygenation characteristics during two types of supplementation by studying SpO2 signal features and assess their performance in hypoxemia risk screening during NICU monitoring.Subjects and methodsSpO2 data from 25 infants with gestational age <32 weeks and birthweight <2,000 g who underwent a cross over trial of low-flow nasal cannula (NC) and digitally-set servo-controlled oxygen environment (OE) supplementations was considered in this secondary analysis. Features pertaining to signal distribution, variability and complexity were estimated and analyzed for differences between the supplementations. Univariate and regularized multivariate logistic regression was applied to identify relevant features and develop screening models for infants likely to experience a critically high number of IH per day of observation. Their performance was assessed using area under receiver operating curves (AUROC), accuracy, sensitivity, specificity and F1 scores.ResultsWhile most SpO2 measures remained comparable during both supplementations, signal irregularity and complexity were elevated while on OE, pointing to more volatility in oxygen saturation during this supplementation mode. In addition, SpO2 variability measures exhibited early prognostic value in discriminating infants at higher risk of critically many IH events. Poincare plot variability at lag 1 had AUROC of 0.82, 0.86, 0.89 compared to 0.63, 0.75, 0.81 for the IH number, a clinical parameter at observation times of 30 min, 1 and 2 h, respectively. Multivariate models with two features exhibited validation AUROC > 0.80, F1 score > 0.60 and specificity >0.85 at observation times ≥ 1 h. Finally, we proposed a framework for risk stratification of infants using a cumulative risk score for continuous monitoring.ConclusionAnalysis of oxygen saturation signal routinely collected in the NICU, may have extensive applications in inferring subtle changes to cardiorespiratory dynamics under various conditions as well as in informing clinical decisions about infant care

Realize, Analyze, Engage (RAE): A Digital Tool to Support Recovery from Substance Use Disorder

Background: Substance use disorders are a highly prevalent group of chronic diseases with devastating individual and public health consequences. Current treatment strategies suffer from high rates of relapse, or return to drug use, and novel solutions are desperately needed. Realize Analyze Engage (RAE) is a digital, mHealth intervention that focusses on real time, objective detection of high-risk events (stress and drug craving) to deploy just-in-time supportive interventions. The present study aims to (1) evaluate the accuracy and usability of the RAE system and (2) evaluate the impact of RAE on patient centered outcomes. Methods: The first phase of the study will be an observational trial of N = 50 participants in outpatient treatment for SUD using the RAE system for 30 days. Accuracy of craving and stress detection algorithms will be evaluated, and usability of RAE will be explored via semi-structured interviews with participants and focus groups with SUD treatment clinicians. The second phase of the study will be a randomized controlled trial of RAE vs usual care to evaluate rates of return to use, retention in treatment, and quality of life. Anticipated findings and future directions: The RAE platform is a potentially powerful tool to de-escalate stress and craving outside of the clinical milieu, and to connect with a support system needed most. RAE also aims to provide clinicians with actionable insight to understand patients\u27 level of risk, and contextual clues for their triggers in order to provide more personalized recovery support

Emergence of local synchronization in neuronal networks with adaptive couplings

Local synchronization, both prolonged and transient, of oscillatory neuronal behavior in cortical networks plays a fundamental role in many aspects of perception and cognition. Here we study networks of Hindmarsh-Rose neurons with a new type of adaptive coupling, and show that these networks naturally produce both permanent and transient synchronization of local clusters of neurons. These deterministic systems exhibit complex dynamics with 1/fη power spectra, which appears to be a consequence of a novel form of self-organized criticality

Casein Kinase 1 Delta (CK1δ) Regulates Period Length of the Mouse Suprachiasmatic Circadian Clock In Vitro

BACKGROUND: Casein kinase 1 delta (CK1delta) plays a more prominent role in the regulation of circadian cycle length than its homologue casein kinase 1 epsilon (CK1epsilon) in peripheral tissues such as liver and embryonic fibroblasts. Mice lacking CK1delta die shortly after birth, so it has not been possible to assess the impact of loss of CK1delta on behavioral rhythms controlled by the master circadian oscillator in the suprachiasmatic nuclei (SCN). METHODOLOGY/PRINCIPAL FINDINGS: In the present study, mPER2::LUCIFERASE bioluminescence rhythms were monitored from SCN explants collected from neonatal mice. The data demonstrate that SCN explants from neonatal CK1delta-deficient mice oscillate, but with a longer circadian period than littermate controls. The cycle length of rhythms recorded from neonatal SCN explants of CK1epsilon-deficient mice did not differ from control explants. CONCLUSIONS/SIGNIFICANCE: The results indicate that CK1delta plays a more prominent role than CK1epsilon in the maintenance of 24-hour rhythms in the master circadian oscillator

Scaling Behavior of Human Locomotor Activity Amplitude: Association with Bipolar Disorder

Scale invariance is a feature of complex biological systems, and abnormality of multi-scale behaviour may serve as an indicator of pathology. The hypothalamic suprachiasmatic nucleus (SCN) is a major node in central neural networks responsible for regulating multi-scale behaviour in measures of human locomotor activity. SCN also is implicated in the pathophysiology of bipolar disorder (BD) or manic-depressive illness, a severe, episodic disorder of mood, cognition and behaviour. Here, we investigated scaling behaviour in actigraphically recorded human motility data for potential indicators of BD, particularly its manic phase. A proposed index of scaling behaviour (Vulnerability Index [VI]) derived from such data distinguished between: [i] healthy subjects at high versus low risk of mood disorders; [ii] currently clinically stable BD patients versus matched controls; and [iii] among clinical states in BD patients

Multi-Scale Motility Amplitude Associated with Suicidal Thoughts in Major Depression

Major depression occurs at high prevalence in the general population, often starts in juvenile years, recurs over a lifetime, and is strongly associated with disability and suicide. Searches for biological markers in depression may have been hindered by assuming that depression is a unitary and relatively homogeneous disorder, mainly of mood, rather than addressing particular, clinically crucial features or diagnostic subtypes. Many studies have implicated quantitative alterations of motility rhythms in depressed human subjects. Since a candidate feature of great public-health significance is the unusually high risk of suicidal behavior in depressive disorders, we studied correlations between a measure (vulnerability index [VI]) derived from multi-scale characteristics of daily-motility rhythms in depressed subjects (n = 36) monitored with noninvasive, wrist-worn, electronic actigraphs and their self-assessed level of suicidal thinking operationalized as a wish to die. Patient-subjects had a stable clinical diagnosis of bipolar-I, bipolar-II, or unipolar major depression (n = 12 of each type). VI was associated inversely with suicidal thinking (r =  –0.61 with all subjects and r =  –0.73 with bipolar disorder subjects; both p<0.0001) and distinguished patients with bipolar versus unipolar major depression with a sensitivity of 91.7% and a specificity of 79.2%. VI may be a useful biomarker of characteristic features of major depression, contribute to differentiating bipolar and unipolar depression, and help to detect risk of suicide. An objective biomarker of suicide-risk could be advantageous when patients are unwilling or unable to share suicidal thinking with clinicians

Point process time–frequency analysis of dynamic respiratory patterns during meditation practice

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heart beats. We propose a robust algorithm for quantifying instantaneous RSA as applied to heart beat intervals and respiratory recordings under dynamic breathing patterns. The blood volume pressure-derived heart beat series (pulse intervals, PIs) are modeled as an inverse Gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PIs and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated via a frequency domain transfer function evaluated at instantaneous respiratory frequency where high coherence between respiration and PIs is observed. The model is statistically validated using Kolmogorov–Smirnov goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. The presented analysis confirms the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states, reporting statistically significant increase in RSA gain as measured by our paradigm.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant K01-AT00694-01