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Background: Repeated opioid exposure leads to a variety of physiologic adaptations that develop at different rates and may foreshadow risk of opioid-use disorder (OUD), including dependence and withdrawal. Digital pharmacovigilance strategies that use noninvasive sensors to identify physiologic adaptations to opioid use represent a novel strategy to facilitate safer opioid prescribing. This study aims to identify wearable sensor-derived features associated with opioid dependence by comparing opioid-naïve individuals to chronic opioid users with acute pain and developing a machine-learning model to distinguish between the 2 groups., Methods: Using a longitudinal observational study design, continuous physiologic data were collected on participants with acute pain receiving opioid analgesia. Monitoring continued throughout hospitalization and for up to 7 days posthospital discharge. Opioid administration data were obtained from electronic health record (EHR) and participant self-report. Participants were classified as belonging to 1 of 3 categories based on opioid use history: naïve, occasional, or chronic use. Thirty features were derived from sensor data, and an additional 9 features were derived from participant demographic and treatment characteristics. Physiologic feature behavior immediately postopioid use was compared among naïve and chronic participants, and subsequently features were used to generate machine learning models which were validated using cross-validation and holdout data., Results: Forty-one participants with a combined total of 169 opioid administrations were ultimately included in the final analysis. Four interpretable decision tree-based machine learning models with 14 sensor-based and 5 clinical features were developed to predict class membership on the level of a given observation (dose) and on the participant level. Ranges for model metrics on the participant level were as follows: accuracy 70% to 90%, sensitivity 67% to 100%, and specificity 67% to 100%., Conclusions: Wearable sensor-derived digital biomarkers can be used to predict opioid use status (naïve versus chronic) and the differentiating features may be detecting opioid dependence. Future work should be aimed at further delineating the phenomenon identified in these models (including opioid dependence and/or withdrawal) and at identifying transition states where an individual changes from 1 profile to another with repetitive opioid exposure., Competing Interests: Conflicts of Interest, Funding: Please see DISCLOSURES at the end of this article., (Copyright © 2024 International Anesthesia Research Society.)

Background: Digital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery., Methods: This was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment. Participants were asked to use the RAE Health app, wear a commercial-grade wrist sensor over a 30-day period. They were asked to self-report stress and craving, at which time were offered brief in-app de-escalation tools. Supervised machine learning algorithms were applied retrospectively to wearable sensor data obtained to develop group-based digital biomarkers for stress and craving. Engagement was assessed by number of days of utilization, and number of hours in a given day of connection., Results: Sixty percent of participants (N=30) completed the 30-day protocol. The model detected stress and craving correctly 76 % and 69 % of the time, respectively, but with false positive rates of 33 % and 28 % respectively. All models performed close to previously validated models from a research grade sensor. Participants used the app for a mean of 14.2 days (SD 10.1) and 11.7 h per day (SD 8.2). Anxiety disorders were associated with higher mean hours per day connected, and return to drug use events were associated with lower mean hours per day connected., Conclusions: Future work should explore the effect of similar digital health systems on treatment outcomes and the optimal dose of digital interventions needed to make a clinically significant impact., Competing Interests: Declaration of Competing Interest JS is employed by RAE Health. SC and PI and are academic partners with RAE Health on two Small Business Innovation Research awards., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: The objective of this study was to examine the association of cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, with late-onset sepsis for extremely preterm infants (<29 weeks of gestational age) on vs off invasive mechanical ventilation., Study Design: This is a retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in 5 level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean gestational age: 26.4 weeks, SD 1.71). Monitoring data were available and analyzed for 719 infants (47 512 patient-days); of whom, 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72 hours after birth and ≥5-day antibiotics)., Results: For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer events with oxygen saturation <80% (IH80) and more bradycardia events before sepsis. IH events were associated with higher sepsis risk but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model including postmenstrual age, cardiorespiratory variables (apnea, periodic breathing, IH80, and bradycardia), and ventilator status predicted sepsis with an area under the receiver operator characteristic curve of 0.783., Conclusion: We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis., Competing Interests: Declaration of Competing Interest Some authors have financial conflicts of interest. JRM and DEL own stock in Medical Prediction Sciences Corporation. JRM is a consultant for Nihon Kohden Digital Health Solutions, proceeds donated to the University of Virginia. ZAV is a consultant for Medtronic. All other authors have no financial conflicts to disclose. No authors have any nonfinancial conflicts of interest to disclose. Funding Support: We acknowledge the following NIH grants for funding the work presented in this manuscript. University of Virginia (NCT03174301): U01 HL133708, K23 HD097254, HL133708-05S1. Case Western Reserve University: U01 HL133643. Northwestern University: U01 HL133704. University of Alabama at Birmingham: U01 HL133536, K23 HL157618. University of Miami: U01 HL133689. Washington University: U01 HL133700, F., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The prevalence of e-cigarette use among young adults in the USA is high (14%). Although the majority of users plan to quit vaping, the motivation to make a quit attempt is low and available support during a quit attempt is limited. Using wearable sensors to collect physiological data (eg, heart rate) holds promise for capturing the right timing to deliver intervention messages. This study aims to fill the current knowledge gap by proposing statistical methods to (1) de-noise beat-to-beat interval (BBI) data from smartwatches worn by 12 young adult regular e-cigarette users for 7 days; and (2) summarize the de-noised data by event and control segments. We also conducted a comprehensive review of conventional methods for summarizing heart rate variability (HRV) and compared their performance with the proposed method. The results show that the proposed singular spectrum analysis (SSA) can effectively de-noise the highly variable BBI data, as well as quantify the proportion of total variation extracted. Compared to existing HRV methods, the proposed second order polynomial model yields the highest area under the curve (AUC) value of 0.76 and offers better interpretability. The findings also indicate that the average heart rate before vaping is higher and there is an increasing trend in the heart rate before the vaping event. Importantly, the development of increasing heart rate observed in this study implies that there may be time to intervene as this physiological signal emerges. This finding, if replicated in a larger scale study, may inform optimal timings for delivering messages in future intervention., (© 2024 John Wiley & Sons Ltd.)

Objective. Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes. Approach . We calculated a subset of 33 HCTSA features on>7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on>3500HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90&#95;DPE (duration per event of intermittent hypoxemia events below 90%). Main Results. The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90&#95;DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90&#95;DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). Significance . These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90&#95;DPE as an optimal predictor of respiratory outcomes., (Creative Commons Attribution license.)

Background: In extremely preterm infants, persistence of cardioventilatory events is associated with long-term morbidity. Therefore, the objective was to characterize physiologic growth curves of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants during the first few months of life., Methods: The Prematurity-Related Ventilatory Control study included 717 preterm infants <29 weeks gestation. Waveforms were downloaded from bedside monitors with a novel sharing analytics strategy utilized to run software locally, with summary data sent to the Data Coordinating Center for compilation., Results: Apnea, periodic breathing, and intermittent hypoxemia events rose from day 3 of life then fell to near-resolution by 8-12 weeks of age. Apnea/intermittent hypoxemia were inversely correlated with gestational age, peaking at 3-4 weeks of age. Periodic breathing was positively correlated with gestational age peaking at 31-33 weeks postmenstrual age. Females had more periodic breathing but less intermittent hypoxemia/bradycardia. White infants had more apnea/periodic breathing/intermittent hypoxemia. Infants never receiving mechanical ventilation followed similar postnatal trajectories but with less apnea and intermittent hypoxemia, and more periodic breathing., Conclusions: Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation., Impact: Physiologic curves of cardiorespiratory events in extremely preterm-born infants offer (1) objective measures to assess individual patient courses and (2) guides for research into control of ventilation, biomarkers and outcomes. Presented are updated maturational trajectories of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in 717 infants born <29 weeks gestation from the multi-site NHLBI-funded Pre-Vent study. Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation. Different time courses for apnea and periodic breathing suggest different maturational mechanisms., (© 2023. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Background: Identifying suicidal risk based on clinical assessment is challenging. Suicidal ideation fluctuates, can be downplayed or denied, and seems stigmatizing if divulged. In contrast, vitality is foundational to subjectivity in being immediately conscious before reflection. Including its assessment may improve detection of suicidal risk compared to relying on suicidal ideation alone. We hypothesized that objective motility measures would be associated with vitality and enhance assessment of suicidal risk., Methods: We evaluated 83 adult-psychiatric outpatients with a DSM-5 bipolar (BD) or major depressive disorder (MDD): BD-I (n = 48), BD-II (20), and MDD (15) during a major depressive episode. They were actigraphically monitored continuously over 3 weekdays and self-rated their subjective states at regular intervals. We applied cosinor analysis to actigraphic data and analyzed associations of subjective psychopathology measures with circadian activity parameters., Results: Actigraphic circadian mesor, amplitude, day- and nighttime activity were lower with BD versus MDD. Self-rated vitality (wish-to-live) was significantly lower, self-rated suicidality (wish-to-die) was higher, and their difference was lower, with BD versus MDD. There were no other significant diagnostic differences in actigraphic sleep parameters or in self-rated depression, dysphoria, or anxiety. By linear regression, the difference between vitality and passive suicidal ideation was strongly positively correlated with mesor (p &lt; 0.0001), daytime activity (p &lt; 0.0001), and amplitude (p = 0.001)., Conclusions: Higher circadian activity measures reflected enhanced levels of subjective vitality and were associated with lesser suicidal ideation. Current suicidal-risk assessment might usefully include monitoring of motility and vitality in addition to examining negative affects and suicidal thinking., (© 2023 S. Karger AG, Basel.)

Introduction: Though the nature of breastfeeding is critical, scant information is available on how the action of the milk transfer from mother to infant is regulated in humans, where the points of dysfunction are, and what can be done to optimize breastfeeding outcomes. While better therapeutic strategies are needed, before they can be devised, a basic scientific understanding of the biomechanical mechanisms that regulate human milk transfer from breast to stomach must first be identified, defined, and understood., Methods: Combining systems biology and systems medicine into a conceptual framework, using engineering design principles, this work investigates the use of biosensors to characterize human milk flow from the breast to the infant's stomach to identify points of regulation. This exploratory study used this framework to characterize Maternal/Infant Lactation physioKinetics (MILK) utilizing a Biosensor ARray (BAR) as a data collection method., Results: Participants tolerated the MILKBAR well during data collection. Changes in breast turgor and temperature were significant and related to the volume of milk transferred from the breast. The total milk volume transferred was evaluated in relation to contact force, oral pressure, and jaw movement. Contact force was correlated with milk flow. Oral pressure appears to be a redundant measure and reflective of jaw movements., Discussion: Nipple and breast turgor, jaw movement, and swallowing were associated with the mass of milk transferred to the infant's stomach. More investigation is needed to better quantify the mass of milk transferred in relation to each variable and understand how each variable regulates milk transfer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Francis, Flynn, Naowar, Indic and Dickton.)

Pulmonary hypertension (PH) is a complex cardiovascular condition associated with multiple morbidities and mortality risk in preterm infants. PH often complicates the clinical course of infants who have bronchopulmonary dysplasia (BPD), a more common lung disease in these neonates, causing respiratory deterioration and an even higher risk of mortality. While risk factors and prevalence of PH are not yet well defined, early screening and management of PH in infants with BPD are recommended by consensus guidelines from the American Heart Association. In this study, we propose a screening method for PH by applying a signal analysis technique to oxygen saturation in infants. Oxygen saturation data from infant groups with BPD (41 with and 60 without PH), recorded prior to their clinical PH diagnosis were analyzed in this study. An information-based similarity approach was applied to quantify the regularity of SpO 2 fluctuations represented as binary words between adjacent five-minute segments. Similarity indices (SI) were observed to be lower in subjects with PH compared to those with BPD alone (p<0.001). These measures were also assessed for performance in screening for PH. SI of 7-bit words, exhibited 80% detection accuracy, 76% sensitivity and specificity of 83%. This index also exhibited a cross-validated mean (SD) F1-score of 0.80 (0.08) ensuring that sensitivity and recall of the screening were balanced. Similarity analysis of oxygen saturation patterns is a novel technique that can be potentially developed into a signal based early PH detection method to support clinical decision and care in this vulnerable population., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Rationale: Immature control of breathing is associated with apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants. However, it is not clear if such events independently predict worse respiratory outcome. Objectives: To determine if analysis of cardiorespiratory monitoring data can predict unfavorable respiratory outcomes at 40 weeks postmenstrual age (PMA) and other outcomes, such as bronchopulmonary dysplasia at 36 weeks PMA. Methods: The Prematurity-related Ventilatory Control (Pre-Vent) study was an observational multicenter prospective cohort study including infants born at <29 weeks of gestation with continuous cardiorespiratory monitoring. The primary outcome was either "favorable" (alive and previously discharged or inpatient and off respiratory medications/O 2 /support at 40 wk PMA) or "unfavorable" (either deceased or inpatient/previously discharged on respiratory medications/O 2 /support at 40 wk PMA). Measurements and Main Results: A total of 717 infants were evaluated (median birth weight, 850 g; gestation, 26.4 wk), 53.7% of whom had a favorable outcome and 46.3% of whom had an unfavorable outcome. Physiologic data predicted unfavorable outcome, with accuracy improving with advancing age (area under the curve, 0.79 at Day 7, 0.85 at Day 28 and 32 wk PMA). The physiologic variable that contributed most to prediction was intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <90%. Models with clinical data alone or combining physiologic and clinical data also had good accuracy, with areas under the curve of 0.84-0.85 at Days 7 and 14 and 0.86-0.88 at Day 28 and 32 weeks PMA. Intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <80% was the major physiologic predictor of severe bronchopulmonary dysplasia and death or mechanical ventilation at 40 weeks PMA. Conclusions: Physiologic data are independently associated with unfavorable respiratory outcome in extremely preterm infants.

Rationale: Bedside biomarkers that allow early identification of infants with bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) are critically important, given the higher risk of death in these infants. Objectives: We hypothesized that infants with BPD-PH have patterns of intermittent hypoxemia (IH) that differ from infants with BPD without PH. Methods: We conducted a matched case-control study of extremely preterm infants from 22 weeks 0 days to 28 weeks 6 days born between 2018 and 2020 at the University of Alabama at Birmingham. BPD-PH status was determined using echocardiographic data performed after postnatal Day 28. Physiologic data were compared between infants with BPD-PH (cases) and BPD alone (control subjects). Receiver operating characteristic (ROC) analysis estimated the predictive ability of cumulative hypoxemia, desaturation frequency, and duration of intermittent hypoxemic events in the week preceding echocardiography to discriminate between cases and control subjects. Measurements and Main Results: Forty infants with BPD-PH were compared with 40 infants with BPD alone. Infants with and without PH had a similar frequency of IH events, but infants with PH had more prolonged hypoxemic events for desaturations below 80% (7 s vs. 6 s; P  = 0.03) and 70% (105 s vs. 58 s; P  = 0.008). Among infants with BPD-PH, infants who died had longer hypoxemic events below 70% (145 s vs. 72 s; P  = 0.01). Using the duration of hypoxemic events below 70%, the areas under the ROC curves for diagnosis of BPD-PH and death in BPD-PH infants were 0.71 and 0.77, respectively. Conclusions: Longer duration of intermittent hypoxemic events was associated both with a diagnosis of BPD-PH and with death among infants with BPD-PH.

Background: Oxygen supplementation is commonly used to maintain oxygen saturation (SpO 2 ) 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 SpO 2 signal features and assess their performance in hypoxemia risk screening during NICU monitoring., Subjects and Methods: SpO 2 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., Results: While most SpO 2 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, SpO 2 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., Conclusion: Analysis 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Ramanand, Indic, Travers and Ambalavanan.)

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.

Hypoxemia, characterized by low blood oxygen levels is pervasive in preterm infants and is associated with development of multiple adverse cardiovascular morbidities. In clinical practice, it is often quantified using frequency, pattern and time spent in it. A predictive tool of hypoxemia occurrence will aid clinicians in risk stratifying infant oxygenation patterns and improving personalized care. As a first step towards this goal in characterizing the underlying temporal processes, we studied inter-hypoxemia interval distributions in preterm infants on oxygen supplementation. We derived regression relationships of characterizing parameters of the distributions with gestational age and birth weight of infants. The modeling and goodness of fit tests of pooled and individual inter-hypoxemia intervals indicated that the inverse Gaussian and Birnbaum Saunders distributions fit well over short time scales and the lognormal at longer time scales. Information from distribution modeling may provide insights into hypoxemia recurrence times and be helpful in developing models to predict severe hypoxemic events that may be translated to personalized care in clinical settings. Clinical relevance - Understanding the stochastic nature of temporal processes underlying hypoxemia in preterm infants is a critical step towards developing predictive models for their occurrence. This may potentially aid in the neonatal care and treatment of these vulnerable infants.

Abstract: Appropriate monitoring of opioid use in patients with pain conditions is paramount, yet it remains a very challenging task. The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning. Participants were recruited from an oral and maxillofacial surgery clinic. Participants were 46 adult patients (26 female) receiving opioids after dental surgery. Participants wore Empatica E4 sensors during the period they self-administered opioids. The E4 collected physiological parameters including accelerometer x-, y-, and z-axes, heart rate, and electrodermal activity. Four machine learning models provided validation accuracies greater than 80%, but the bagged-tree model provided the highest combination of validation accuracy (83.7%) and area under the receiver operating characteristic curve (0.92). The trained model had a validation sensitivity of 82%, a specificity of 85%, a positive predictive value of 85%, and a negative predictive value of 83%. A subsequent test of the trained model on withheld data had a sensitivity of 81%, a specificity of 88%, a positive predictive value of 87%, and a negative predictive value of 82%. Results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment., (Copyright © 2021 International Association for the Study of Pain.)

Background: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson's disease (PD). Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD. Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls ( N = 20 each). PD subjects with normal (PDN, MoCA 26-30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis. Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status ( p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status ( p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage. Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Novak, Chase, Narayanan, Indic and Markopoulou.)

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' level of risk, and contextual clues for their triggers in order to provide more personalized recovery support., Competing Interests: CONFLICTS OF INTEREST Megan Reinhardt is the CEO of RAE Health and Nicole Gilbertson is the COO of RAE Health. They represent the small business partners in this small business innovation research award.

The possible mechanisms for the synchronization of rest-activity rhythms of individual animals living in groups is a relatively understudied question; synchronized rhythms could occur by entrainment of individuals to a common external force and/or by social synchronization between individuals. To gain insight into this question, we explored the synchronization dynamics of populations of globally coupled Kuramoto oscillators and analyzed the effects of a finite oscillator number (N) and the variable strengths of their periodic forcing (F) and mutual coupling (K). We found that increasing N promotes entrainment to a decreasing value of F, but that F could not be reduced below a certain level determined by the number of oscillators and the distribution width of their intrinsic frequencies. Our analysis prompts some simple predictions of ecologically optimal animal group sizes under differing natural conditions.

Aims: To determine the accuracy of a wearable sensor to detect and differentiate episodes of self-reported craving and stress in individuals with substance use disorders, and to assess acceptability, barriers, and facilitators to sensor-based monitoring in this population., Methods: This was an observational mixed methods pilot study. Adults enrolled in an outpatient treatment program for a substance use disorder wore a non-invasive wrist-mounted sensor for four days and self-reported episodes of stress and craving. Continuous physiologic data (accelerometry, skin conductance, skin temperature, and heart rate) were extracted from the sensors and analyzed via various machine learning algorithms. Semi-structured interviews were conducted upon study completion, and thematic analysis was conducted on qualitative data from semi-structured interviews., Results: Thirty individuals completed the protocol, and 43 % (N = 13) were female. A total of 41 craving and 104 stress events were analyzed. The differentiation accuracies of the top performing models were as follows: stress vs. non-stress states 74.5 % (AUC 0.82), craving vs. no-craving 75.7 % (AUC 0.82), and craving vs. stress 76.8 % (AUC 0.8). Overall participant perception was positive, and acceptability was high. Emergent themes from the exit interviews included a perception of connectedness and increased mindfulness related to wearing the sensor, both of which were reported as helpful to recovery. Barriers to engagement included interference with other daily wear items, and perceived stigma., Conclusions: Wearable sensors can be used to objectively differentiate episodes of craving and stress, and individuals in recovery from substance use disorder are accepting of continuous monitoring with these devices., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to disclose., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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 were 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.

Background: The increasing incidence of bronchopulmonary dysplasia in premature babies may be due in part to immature ventilatory control, contributing to hypoxemia. The latter responds to ventilation and/or oxygen therapy, treatments associated with adverse sequelae. This is an overview of the Prematurity-Related Ventilatory Control Study which aims to analyze the under-utilized cardiorespiratory continuous waveform monitoring data to delineate mechanisms of immature ventilatory control in preterm infants and identify predictive markers., Methods: Continuous ECG, heart rate, respiratory, and oxygen saturation data will be collected throughout the NICU stay in 500 infants < 29 wks gestation across 5 centers. Mild permissive hypercapnia, and hyperoxia and/or hypoxia assessments will be conducted in a subcohort of infants along with inpatient questionnaires, urine, serum, and DNA samples., Results: Primary outcomes will be respiratory status at 40 wks and quantitative measures of immature breathing plotted on a standard curve for infants matched at 36-37 wks. Physiologic and/or biologic determinants will be collected to enhance the predictive model linking ventilatory control to outcomes., Conclusions: By incorporating bedside monitoring variables along with biomarkers that predict respiratory outcomes we aim to elucidate individualized cardiopulmonary phenotypes and mechanisms of ventilatory control contributing to adverse respiratory outcomes in premature infants.

Long-term recordings of movement in preterm infants might reveal important clinical information. However, measurement of movement is limited because of time-consuming and subjective analysis of video or reluctance to attach additional sensors to the infant. We evaluated whether photoplethysmogram (PPG), routinely used for oximetry in preterm infants in the neonatal intensive care unit (NICU), can provide reliable long-term measurements of movement. In 18 infants [mean post-conceptional age (PCA) 31.10 weeks, range 29-34.29 weeks], we designed and tested a wavelet-based algorithm that detects movement signals from the PPG. The algorithm's performance was optimized relative to subjective assessments of movement using video and accelerometers attached to two limbs and force sensors embedded within the mattress (five infants, three raters). We then applied the optimized algorithm to infants receiving routine care in the NICU without additional sensors. The algorithm revealed a decline in brief movements (< 5 s) with increasing PCA (13 infants, r = - 0.87, p < 0.001, PCA range 27.3-33.9 weeks). Our findings suggest that quantitative relationships between motor activity and clinical outcomes in preterm infants can be studied using routine photoplethysmography.

Current understanding of the pathogenesis of the familial form of amyotrophic lateral sclerosis has been aided by the study of transgenic mice that over-express mutated forms of the human CuZn-superoxide dismutase (SOD1) gene. While mutant SOD1 in motor neurons determines disease onset, other non-cell autonomous factors are critical for disease progression, and altered energy metabolism has been implicated as a contributing factor. Since most energy expended by laboratory mice is utilized to defend body temperature (T b ), we analyzed thermoregulation in transgenic mice carrying the G93A mutation of the human SOD1 gene, using implantable temperature data loggers to continuously record T b for up to 85 days. At room (22 °C) ambient temperature, G93A mice exhibited a diminished amplitude of the daily T b rhythm compared to C57BL/6J controls, secondary to decreased T b values during the dark (behaviorally active) phase of the light-dark cycle. The defect arose at 85-99 days of age, around the age of symptom onset (as assessed by grip strength), well before observable weakness and weight loss, and could not be accounted for by decreased levels of locomotor activity or food consumption. Housing under thermoneutral (29 °C) ambient temperature partially rescued the defect, but age-dependently (only in animals >100 days of age), suggesting that the deficit in older mice was due in part to inadequate thermogenesis by "peripheral" thermogenic organs as the disease progressed. In younger mice, we found that cold-induced thermogenesis and energy expenditure were intact, hinting that an initial "central" defect might localize to the subparaventricular zone, involving neural output pathways from the circadian clock in the hypothalamic suprachiasmatic nucleus to forebrain thermoregulatory circuitry., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objective: To test the hypothesis that environmental compared with nasal cannula oxygen decreases episodes of intermittent hypoxemia (oxygen saturations <85% for ≥10 seconds) in preterm infants on supplemental oxygen by providing a more stable hypopharyngeal oxygen concentration., Study Design: This was a single center randomized crossover trial with a 1:1 parallel allocation to order of testing. Preterm infants on supplemental oxygen via oxygen environment maintained by a servo-controlled system or nasal cannula with flow rates ≤1.0 L per kg per minute were crossed over every 24 hours for 96 hours. Data were collected electronically to capture real time numeric and waveform data from patient monitors., Results: Twenty-five infants with gestational age of 27 ± 2 weeks (mean ± SD) and a birth weight of 933 ± 328 g were studied at postnatal day 36 ± 26. The number of episodes of intermittent hypoxemia per 24 hours was 117 ± 77 (median, 98; range, 4-335) with oxygen environment vs 130 ± 63 (median, 136; range, 16-252) with nasal cannula (P = .002). Infants on oxygen environment compared with nasal cannula also had decreased episodes of severe intermittent hypoxemia (P = .005). Infants on oxygen environment compared with nasal cannula had a lower proportion of time with oxygen saturations <85% (.05 ± .03 vs .06 ± .03, P < .001), and a lower coefficient of variation of oxygen saturation (P = .02)., Conclusions: In preterm infants receiving supplemental oxygen, servo-controlled oxygen environment decreases hypoxemia compared with nasal cannula., Trial Registration: ClinicalTrials.gov: NCT02794662., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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.

Opioid abuse is a rapidly escalating problem in the United States. Effective opioid reversal is achieved with the antidote naloxone, but often does not last as long as the offending opioid, necessitating in-hospital observation. Continuous physiologic monitoring using wearable biosensors represents a potential option to extend monitoring capability outside the clinical setting across the spectrum of opioid abuse including post- naloxone administration. The present study aims to identify the physiologic change that marks the cessation of naloxone's effect. Eleven participants were recruited in the Emergency Department after naloxone administration for an opioid overdose and continuously monitored using a wearable biosensor measuring heart rate, temperature, electrodermal activity and accelerometry. Hilbert transform was used to evaluate a 90- minute post naloxone time point. Physiologic changes were consistent with the onset of opioid drug effect across parameters, but only changes in heart rate and skin temperature research statistical significance.

Objective: Episodes of bradycardia are common and recur sporadically in preterm infants, posing a threat to the developing brain and other vital organs. We hypothesize that bradycardias are a result of transient temporal destabilization of the cardiac autonomic control system and that fluctuations in the heart rate signal might contain information that precedes bradycardia. We investigate infant heart rate fluctuations with a novel application of point process theory., Methods: In ten preterm infants, we estimate instantaneous linear measures of the heart rate signal, use these measures to extract statistical features of bradycardia, and propose a simplistic framework for prediction of bradycardia., Results: We present the performance of a prediction algorithm using instantaneous linear measures (mean area under the curve = 0.79 ± 0.018) for over 440 bradycardia events. The algorithm achieves an average forecast time of 116 s prior to bradycardia onset (FPR = 0.15). Our analysis reveals that increased variance in the heart rate signal is a precursor of severe bradycardia. This increase in variance is associated with an increase in power from low content dynamics in the LF band (0.04-0.2 Hz) and lower multiscale entropy values prior to bradycardia., Conclusion: Point process analysis of the heartbeat time series reveals instantaneous measures that can be used to predict infant bradycardia prior to onset., Significance: Our findings are relevant to risk stratification, predictive monitoring, and implementation of preventative strategies for reducing morbidity and mortality associated with bradycardia in neonatal intensive care units.

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.

Objective: To examine the therapeutic potential of stochastic vibrotactile stimulation (SVS) as a complementary non-pharmacological intervention for withdrawal in opioid-exposed newborns., Study Design: A prospective, within-subjects single-center study was conducted in 26 opioid-exposed newborns (>37 weeks; 16 male) hospitalized since birth and treated pharmacologically for Neonatal Abstinence Syndrome. A specially-constructed mattress delivered low-level SVS (30-60Hz, 10-12μm RMS), alternated in 30-min intervals between continuous vibration (ON) and no vibration (OFF) over a 6-8 hr session. Movement activity, heart rate, respiratory rate, axillary temperature and blood-oxygen saturation were calculated separately for ON and OFF., Results: There was a 35% reduction in movement activity with SVS (p<0.001), with significantly fewer movement periods >30 sec duration for ON than OFF (p = 0.003). Incidents of tachypneic breaths and tachycardic heart beats were each significantly reduced with SVS, whereas incidents of eupneic breaths and eucardic heart beats each significantly increased with SVS (p<0.03). Infants maintained body temperature and arterial-blood oxygen level independent of stimulation condition., Conclusions: SVS reduced hyperirritability and pathophysiological instabilities commonly observed in pharmacologically-managed opioid-exposed newborns. SVS may provide an effective complementary therapeutic intervention for improving autonomic function in newborns with Neonatal Abstinence Syndrome.

Introduction: Opioid analgesic use is a major cause of morbidity and mortality in the US, yet effective treatment programs have a limited ability to detect relapse. The utility of current drug detection methods is often restricted due to their retrospective and subjective nature. Wearable biosensors have the potential to improve detection of relapse by providing objective, real time physiologic data on opioid use that can be used by treating clinicians to augment behavioral interventions., Methods: Thirty emergency department (ED) patients who were prescribed intravenous opioid medication for acute pain were recruited to wear a wristband biosensor. The biosensor measured electrodermal activity, skin temperature and locomotion data, which was recorded before and after intravenous opioid administration. Hilbert transform analyses combined with paired t-tests were used to compare the biosensor data A) within subjects, before and after administration of opioids; B) between subjects, based on hand dominance, gender, and opioid use history., Results: Within subjects, a significant decrease in locomotion and increase in skin temperature were consistently detected by the biosensors after opioid administration. A significant change in electrodermal activity was not consistently detected. Between subjects, biometric changes varied with level of opioid use history (heavy vs. nonheavy users), but did not vary with gender or type of opioid. Specifically, heavy users demonstrated a greater decrease in short amplitude movements (i.e. fidgeting movements) compared to non-heavy users., Conclusion: A wearable biosensor showed a consistent physiologic pattern after ED opioid administration and differences between patterns of heavy and non-heavy opioid users were noted. Potential applications of biosensors to drug addiction treatment and pain management should be studied further., Competing Interests: Compliance with Ethical Standards Funding NIH NIDA R01DA033769-01, L30 DA038357, NIH NIDA 1R01DA033323-01, and NIH NCATS 5UL1TR000161-04. Conflicts of Interest The authors have no conflicts to disclose.

Accurate estimation of heart rate dynamics in preterm infants is important for predicting recurrent episodes of severe bradycardia. We hypothesize that estimation of heart rate can be improved by including respiration as a state variable, based on mechanisms that underlie cardio-respiratory coherence. For ten preterm infants, we demonstrate that including respiration as a covariate improves estimation accuracy by an average of 11% across bradycardia severity, and reduces the maximum error by 8%. We also find that cardio-respiratory coherence increases in low frequency content just prior to severe bradycardia. Thus, incorporating respiratory information may improve models of heart rate dynamics and narrow potential features for bradycardia prediction.

Premature infants are susceptible to a variety of life-threatening events. Underdeveloped cardiovascular control due to an immature autonomic nervous system can lead to recurrent bradycardias that reduce blood flow and oxygen to critical organs, and result in long-term developmental disabilities or sudden death. In this study, we investigate the use of a novel point process framework to model heart rate dynamics in premature infants, including the full range of bradycardia severity. We find that the lognormal distribution accurately models the R-R interval time series, due to the long-tail nature of the distribution. We also find that the degree of bradycardia severity is correlated with distinct clustering features of the point-process indices in regions encompassing and adjacent to bradycardias. This underlying property in heart rate dynamics may provide valuable statistical information for quantifying the vulnerability of premature infants to develop bradycardia.

Communal animals often engage in group activities that require temporal synchrony among its members, including synchrony on the circadian timescale. The principles and conditions that foster such collective synchronization are not understood, but existing literature hints that the number of interacting individuals may be a critical factor. We tested this by recording individual circadian body temperature rhythms of female house mice housed singly, in twos (pairs), or in groups of five (quintets) in constant darkness; determining the daily phases of the circadian peak for each animal; and then calculating the cycle-to-cycle phase relationship between cohabiting animals over time. Significant temporal coherence was observed in quintets: the proportion of quintets (4/7), but not pairs (2/8), that became synchronized was greater than could be achieved by the complete simulated reassortment of all individuals. We speculate that the social coupling of individual circadian clocks of group members may be adaptive under certain conditions, and we propose that optimal group sizes in nature may depend not only on species-specific energetics, spatial behaviour and natural history but also on the mathematics of synchronizing assemblies of weakly coupled animal oscillators., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)

Background: Kangaroo care, i.e., skin-to-skin cohabitation (SSC) between an infant and caregiver, is often used in neonatal intensive care units to promote bonding, breastfeeding and infant growth. The direct salutary effects of SSC on cardio-respiratory control in preterm infants remain equivocal; some reports suggest improved breathing stability, others indicate worsening of apnea, bradycardia and hypoxemia., Aim: The purpose of this study was to investigate physiological relationships between the infant and caregiver during SSC. We hypothesized that respiratory stability of the premature infant is influenced by the caregiver's heartbeat., Design: A prospective study was performed in eleven preterm infants (6 female; mean PCA 32 wks). SSC was compared to a preceding incubator-control period (CTL) matched for time from feed and condition duration. Abdominal respiratory movement, electrocardiogram, skin temperature and blood-oxygen levels were recorded from the infant and the caregiver., Results: During CTL, infant interbreath interval variance (IBIv; respiratory instability) was directly related to its own heart rate variance (HRv; rho=0.770, p=0.009). During SSC, infant IBIv and apnea incidence were each related to caregiver HRv (rho 0.764, p=0.006; rho 0.677, p=0.022, respectively). Infant cardio-respiratory coupling was also enhanced during SSC compared to CTL in the eupneic frequency range (0.7-1.5 Hz, p=0.018) and reduced for slower frequencies (0.15-0.45 Hz; p=0.036)., Conclusion: These findings suggest that during SSC, respiratory control of the premature infant is influenced by the caregiver's cardiac rhythm. We propose that the caregiver's heartbeat causes sensory perturbations of the infant via somatic or other afferents, revealing a novel cohabitation-induced feed-back mechanism of respiratory control in the neonate., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

A number of field and laboratory studies have shown that the social environment influences daily rhythms in numerous species. However, underlying mechanisms, including the circadian system's role, are not known. Obstacles to this research have been the inability to track and objectively analyse rhythms of individual animals housed together. Here, we employed temperature dataloggers to track individual body temperature rhythms of pairs of cohabiting male Syrian hamsters (Mesocricetus auratus) in constant darkness and applied a continuous wavelet transform to determine the phase of rhythm onset before, during, and after cohabitation. Cohabitation altered the predicted trajectory of rhythm onsets in 34% of individuals, representing 58% of pairs, compared to 12% of hamsters single-housed as 'virtual pair' controls. Deviation from the predicted trajectory was by a change in circadian period (τ), which tended to be asymmetric-affecting one individual of the pair in nine of 11 affected pairs-with hints that dominance might play a role. These data implicate a change in the speed of the circadian clock as one mechanism whereby social factors can alter daily rhythms. Miniature dataloggers coupled with wavelet analyses should provide powerful tools for future studies investigating the principles and mechanisms mediating social influences on daily timing.

In neonatal research, physiological signals are often degraded by an artifact generated by movement of the infant. Portions of these movement embedded signals are commonly excluded in the analysis of the relevant physiological signal. However, movement may be a significant marker of physiological development of the infant. Here we present results from a wavelet-based algorithm that quantifies neonatal movement, using recordings from the pulse plethysmograph. We suggest that movement-induced artifactual signal can yield important physiological information regarding neonatal physiology.

Interbreath interval (IBI), the time interval between breaths, is an important measure used to analyze irregular breathing patterns in neonates. The discrete bursts of neural activity generate the IBI time series, which exhibits stochastic as well as deterministic dynamics. To quantify the irregularity of breathing, we propose a point process model of IBI using a comprehensive stochastic dynamic modeling framework. The IBIs of immature breathing patterns exhibit a long tail distribution and within a point process model, we have considered the lognormal distribution to represent the stochastic IBI characteristics. An autoregressive (AR) function is embedded within the model to capture the short-term IBI dynamics including abrupt IBI prolongations related to sporadic and periodic apneas that are common in neonates. We tested the utility of our paradigm for depicting the respiratory dynamics in neonatal rats and in preterm infants. Kolmogorov-Smirnov (KS) and independence tests reveal that the model accurately tracks the dynamic characteristics of the signals. In preterm infants, our model-derived indices of IBI instability strongly correlate with clinically derived indices of maturation. Our results validate a new class of algorithms, based on the point process theory, for defining instantaneous measures of breathing irregularity in neonates.

Objectives: Plausible candidates of psychopathological phenomena that may associate with or anticipate suicidal risk, include negative affects, including admixtures of dysphoria, depression and anxiety described mainly in nonpsychotic disorders. We ascertained the distribution of such affective features in various first-episode psychotic disorders and correlated these and other clinical and antecedent features with intake suicidal status., Methods: We evaluated 516 adult subjects in first-lifetime episodes of various DSM-IV-TR psychotic disorders. Blinded, protocol-guided, assessments of clinical features ascertained in SCID examinations, self- and family reports and clinical records supported analyses of associations of suicide attempts at first-psychotic episodes with antecedent and intake clinical characteristics, including negative affects and diagnoses, using standard bivariate and multivariate methods., Results: Negative affective features in various combinations were prevalent (90%) and at >75% in both affective and nonaffective psychotic disorders; anxious depression was most common (22%). We identified antecedent and intake clinical factors preliminarily associated with suicide attempts. Factors remaining independently associated in multivariate logistic modelling (ranked by OR) were: (a) prior suicide attempt, (b) prior aggressive assault, (c) bipolar-mixed state or psychotic major depression diagnosis, (d) prior dysphoria, (e) intake dysphoric-anxiousdepression, (f) prior impulsivity, (g) previous affective instability, (h) previous nonpsychotic depression, (i) previous decline in vital drive, and (j) prior sleep disturbances., Conclusions: Various types and combinations of negative affective features (especially anxious depression with and without dysphoria) were prevalent across nonaffective as well as affective first psychotic episodes and strongly associated with suicide attempts. These findings extend previous observations in nonpsychotic disorders.

A variety of methods to determine phase markers and period length from experimental data sets have traditionally assumed a rhythm of fixed period and amplitude. But most biological oscillations exhibit fluctuations in both period and amplitude, leading to the recent interest in the application of wavelet transforms that can measure how rhythms vary over time. Here we examine how wavelet-based methods can be extended to the analysis of conventional actograms, including the detection of onsets in circadian activity and temperature rhythms of rodents.

Integration of several approaches concerning time and temporality can enhance the pathophysiological study of major mood disorders of unknown etiology. We propose that these conditions might be interpreted as disturbances of temporal profile of biological rhythms, as well as alterations of time-consciousness. Useful approaches to study time and temporality include philological suggestions, phenomenological and psychopathological conceptualizatíons, clinical descriptions, and research on circadian and ultradían rhythms, as well as nonlinear dynamics approaches to their analysis.

Temporal lobe epilepsy has been known to be associated with buildup of 4-7 Hz activity preceded by attenuation near the seizure focus. Using a wavelet based algorithm, we recently showed that for the patients with temporal lobe epilepsy, frequency flow on the scalp EEG builds up to 5-12 Hz range just prior to and during the initial stages of the seizure. Here we present frequency flow analysis on EEG of patients with extra-temporal partial epilepsy and patients with idiopathic generalized epilepsy (IGE) to investigate any characteristic frequency flow patterns in these patients. We found that frequency flow in these patients also stays sustained in the 5-12 Hz range for longer periods of time just prior to and during the initial stages of the seizure compared to their respective baseline interictal recordings. The 5-12 Hz frequency flow was seen uniformly in all the channels in patients with IGE although it was seen most prominently adjacent to the seizure focus and to a lesser extent in other channels in patients with partial epilepsy., (Copyright © 2011 Elsevier B.V. All rights reserved.)

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.

The suprachiasmatic nucleus (SCN) is the master circadian clock in mammals and is composed of thousands of neuronal oscillators expressing different intrinsic periods. These oscillators form a coupled network with a free-running period around 24 h in constant darkness and entrainable to the external light-dark cycle (T cycle). Coupling plays an important role in setting the period of the network and its range of entrainment. Experiments in rats have shown that two subgroups of oscillators within the SCN, a ventrolateral (VL) subgroup that receives photic input and a dorsomedial (DM) subgroup that is coupled to VL, can be desynchronized under a short (22-h) T cycle, with VL entrained to the cycle and DM free-running. We use a modified Goodwin model to understand how entrainment of the subgroups to short (22-h) and long (26-h) T cycles is influenced by light intensity, the proportion of neurons that receives photic input, and coupling heterogeneity. We find that the model's critical value for the proportion of photically-sensitive neurons is in accord with actual experimental estimates, while the model's inclusion of dispersed coupling can account for the experimental observation that VL and DM desynchronize more readily under the 22-h than under the 26-h T cycle. Heterogeneous intercellular coupling within the SCN is likely central to the generation of complex behavioral patterns.

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.

Although much is known about the regulation of the circadian rest-activity cycle by the hypothalamic suprachiasmatic nucleus in nocturnal rodents, little is known about the neural substrates that regulate the temporal organization of nocturnal activity within the active phase. In this report, data are presented in Syrian hamsters to implicate the habenula - believed to be involved in motivation, reward and motor control--as a candidate site for such a role. First, by examining hamsters during the day and night and by introducing a 'novel' running wheel in order to induce daytime motor activity, we showed that immunoreactive c-Fos expression in the lateral and medial habenula is related to motor activity/arousal. Second, by transecting the habenula's major efferent pathway (fasciculus retroflexus), we showed that the interruption of habenula neural output alters the daily amount of motor activity, lengthens the period of the circadian rest-activity rhythm and disrupts the species-typical pattern of nocturnal motor activity, measured as either wheel-running behavior or general locomotor activity. Instead of the usual pattern of night-time locomotion, characterized by a prolonged bout of elevated activity in the early night followed by shorter sporadic bouts or the cessation of activity altogether, lesioned animals exhibited a more homogeneous, undifferentiated temporal profile extending across the night. These data suggest a previously unrecognized function of the habenula whereby it regulates the temporal pattern of activity occurring within a circadian rest-activity window set by the suprachiasmatic nucleus., (© 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.)

Background: Cardio-respiratory interactions are weak at the earliest stages of human development, suggesting that assessment of their presence and integrity may be an important indicator of development in infants. Despite the valuable research devoted to infant development, there is still a need for specifically targeted standards and methods to assess cardiopulmonary functions in the early stages of life. We present a new methodological framework for the analysis of cardiovascular variables in preterm infants. Our approach is based on a set of mathematical tools that have been successful in quantifying important cardiovascular control mechanisms in adult humans, here specifically adapted to reflect the physiology of the developing cardiovascular system., Methods: We applied our methodology in a study of cardio-respiratory responses for 11 preterm infants. We quantified cardio-respiratory interactions using specifically tailored multivariate autoregressive analysis and calculated the coherence as well as gain using causal approaches. The significance of the interactions in each subject was determined by surrogate data analysis. The method was tested in control conditions as well as in two different experimental conditions; with and without use of mild mechanosensory intervention., Results: Our multivariate analysis revealed a significantly higher coherence, as confirmed by surrogate data analysis, in the frequency range associated with eupneic breathing compared to the other ranges., Conclusions: Our analysis validates the models behind our new approaches, and our results confirm the presence of cardio-respiratory coupling in early stages of development, particularly during periods of mild mechanosensory intervention, thus encouraging further application of our approach., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

Objective: EEG data during temporal lobe seizures have been reported to show lateralized buildup of theta activity. However the exact dynamics of the theta activity and its clinical significance are not known. In this work we present an approach using wavelets to study the frequency flow dynamics of this buildup., Methods: We employ continuous wavelet transform to obtain a time frequency representation of the EEG signal. Using a ridge extraction algorithm, the instantaneous frequency is estimated from the normalized scalogram., Result: We found that prior to the seizure onset, frequency flow builds up to 5-12 Hz range and the duration for which the frequency remains in this range gradually increases soon after the seizure onset. We also observed buildup at the adjacent regions. Such buildup characteristics are not seen during baseline conditions of the same patients., Conclusions: Simultaneous buildup of frequency at the temporal and the adjacent regions indicates that during seizure the neuronal interactions propagate over large regions of the brain., Significance: Given that activity in the 5-12 Hz frequency range is seen often in the more alert state, our findings suggest that the brain might be in a transient alert state prior to the epileptic seizure., (Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)