Characterization of Cortisol Dysregulation in Fibromyalgia and Chronic Fatigue Syndromes: A State-Space Approach

Unveiling productivity: The interplay of cognitive arousal and expressive typing in remote work

Cognitive Arousal, frequently elicited by environmental stressors that exceed personal coping resources, manifests in measurable physiological markers, notably in galvanic skin responses. This effect is prominent in cognitive tasks such as composition, where fluctuations in these biomarkers correlate with individual expressiveness. It is crucial to understand the nexus between cognitive arousal and expressiveness. However, there has not been a concrete study that investigates this inter-relation concurrently. Addressing this, we introduce an innovative methodology for simultaneous monitoring of these elements. Our strategy employs Bayesian analysis in a multi-state filtering format to dissect psychomotor performance (captured through typing speed), galvanic skin response or skin conductance (SC), and heart rate variability (HRV). This integrative analysis facilitates the quantification of expressive behavior and arousal states. At the core, we deploy a state-space model connecting one latent psychological arousal condition to neural activities impacting sweating (inferred through SC responses) and another latent state to expressive behavior during typing. These states are concurrently evaluated with model parameters using an expectation-maximization algorithms approach. Assessments using both computer-simulated data and experimental data substantiate the validity of our approach. Outcomes display distinguishable latent state patterns in expressive typing and arousal across different computer software used in office management, offering profound implications for Human-Computer Interaction (HCI) and productivity analysis. This research marks a significant advancement in decoding human productivity dynamics, with extensive repercussions for optimizing performance in telecommuting scenarios.

Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors

Goal: Inferring autonomous nervous system (ANS) activity is a challenging issue and has critical applications in stress regulation. Sweat secretions caused by ANS activity influence the electrical conductance of the skin. Therefore, the variations in skin conductance (SC) measurements reflect the sudomotor nerve activity (SMNA) and can be used to infer the underlying ANS activity. These variations are strongly correlated with emotional arousal as well as thermoregulation. However, accurately recovering ANS activity and the corresponding state-space system from a single channel signal is difficult due to artifacts introduced by measurement noise. To minimize the impact of noise on inferring ANS activity, we utilize multiple channels of SC data. Methods: We model skin conductance using a second-order differential equation incorporating a time-shifted sparse impulse train input in combination with independent cubic basis spline functions. Finally, we develop a block coordinate descent method for SC signal decomposition by employing a generalized cross-validation sparse recovery approach while including physiological priors. Results: We analyze the experimental data to validate the performance of the proposed algorithm. We demonstrate its capacity to recover the ANS activations, the underlying physiological system parameters, and both tonic and phasic components. Finally, we present an overview of the algorithm's comparative performance under varying conditions and configurations to substantiate its ability to accurately model ANS activity. Our results show that our algorithm performs better in terms of multiple metrics like noise performance, AUC score, the goodness of fit of reconstructed signal, and lower missing impulses compared with the single channel decomposition approach. Conclusion: In this study, we highlight the challenges and benefits of concurrent decomposition and deconvolution of multichannel SC signals.

Characterizing Alterations in Cortisol Secretion During Cardiac Surgery

Cortisol is a neuroendocrine hormone of the hypothalamus-pituitary-adrenal (HPA) axis secreted from adrenal glands in response to stimulation by adrenocorticotropic hormone (ACTH) from the anterior pituitary and corticotropin releasing hormone (CRH) from the hypothalamus. Cortisol has multiple functionalities in maintaining bodily homeostasis - including anti-inflammatory influences - through its diurnal secretion pattern (which has been studied extensively); its secretion is also increased in response to major traumatic events such as surgery. Due to the adverse health consequences of an abnormal immune response, it is crucial to understand the effect of cortisol in modulating inflammation. To address this physiological issue, we characterize the secretion of cortisol using a high temporal resolution dataset of ten patients undergoing coronary arterial bypass grafting (CABG) surgery, in comparison with a control group not undergoing surgery. We find that cortisol exhibits different pulsatile dynamics in those undergoing cardiac surgery compared to the control subjects. We also summarize the causality of cortisol's relationship with different cytokines (which are one type of inflammatory markers) by performing Granger causality analysis.Clinical relevance- This work documents time-varying patterns of the HPA axis hormone cortisol in the inflammatory response to cardiac surgery and may eventually help improve patients' prognosis post-surgery (or in other conditions) by enabling early detection of an abnormal cortisol or inflammatory response and enabling patient specific remedial interventions.

A Point-Process Approach for Tracking Valence using a Respiration Belt

Emotional valence is difficult to be inferred since it is related to several psychological factors and is affected by inter- and intra-subject variability. Changes in emotional valence have been found to cause a physiological response in respiration signals. In this study, we propose a state-space model and decode the valence by analyzing a person's respiration pattern. Particularly, we generate a binary point process based on features that are indicative of changes in respiration pattern as a result of an emotional valence response. High valence is typically associated with faster and deeper breathing. As a result, (i)depth of breath, (ii)rate of respiration, and (iii) breathing cycle time are indicators of high valence and used to generate the binary point process representing underlying neural stimuli associated with changes in valence. We utilize an expectation-maximization (EM) framework to decode a hidden valence state and the associated valence index. This predicted valence state is compared to self-reported valence ratings to optimize the parameters and determine the accuracy of the model. The accuracy of the model in predicting high and low valence events is found to be 77% and 73%, respectively. Our study can be applied towards the long term analysis of valence. Additionally, it has applications in a closed-loop system procedures and wearable design paradigm to track and regulate the emotional valence.

Characterization of Leptin Secretion in Premenopausal Obese Women Treated with Bromocriptine

Leptin, a hormone secreted by adipose tissue, is primarily responsible for inhibiting hunger and maintaining energy balance. Improper leptin secretion may result in hyperleptinemia (excess secretion of leptin) or leptin resistance, both of which contribute to obesity. Diagnosing abnormal leptin secretion may help treat this underlying cause of obesity. Therefore, continuous monitoring of the level of leptin may help characterize its secretion dynamics and also help devise an appropriate treatment. In this research, we consider leptin hormone concentration data taken over a 24 hour time period from eighteen healthy premenopausal obese women before and after treatment with a dopamine agonist, bromocriptine, and deconvolve the observed leptin hormone levels to estimate the number, timing, and magnitude of the underlying leptin secretory pulses. We find that there is an overall decrease in leptin secretion, particularly during sleep, but the changes in the secretory and clearance rates, and the number of pulses underlying the secretion process are not statistically significant.Clinical relevance- This work seeks to understand the effect of bromocriptine on leptin secretory dynamics and will help further current understanding of the effect of bromocriptine in relation to obesity.

Sparse Deconvolution and Causality Analysis of Inflammatory Markers During Cardiac Surgery

Major bodily trauma such as cardiac surgery elicits (in response to tissue injury and other exogenous surgical factors) a whole-body inflammation response during which specialized signaling proteins called cytokines are synthesized and invoke multiple defense mechanisms. Many proinflammatory and anti-inflammatory cytokines such as interleukins (IL) and tumor necrosis factor (TNF) are produced to initiate bodily repair. Due to the adverse health consequences, including mortality, of a maladaptive cytokine response, understanding their complex dynamics using system-theoretic modeling and analysis may pave the way for controlling the inflammatory response which may eventually improve medical outcomes for patients. To this end, we use clinical data from ten patients undergoing coronary arterial bypass graft surgery to study the response of four cytokines (IL6, IL8, IL10, TNFα) and the neuroendocrine hormone cortisol. We perform deconvolution to obtain the secretory pulses underlying their pulsatile production and analyze causal interactions, mathematically uncovering some interactive relationships found in previous experimental studies.Clinical relevance- This work is a first step towards a mechanistic inference of the inflammatory response to surgery that could eventually help control the inflammatory response and could inform medical interventions to improve patient outcomes.

Perspective: Drawing on Findings From Critical Illness to Explain Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

We propose an initial explanation for how myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) could originate and perpetuate by drawing on findings from critical illness research. Specifically, we combine emerging findings regarding (a) hypoperfusion and endotheliopathy, and (b) intestinal injury in these illnesses with our previously published hypothesis about the role of (c) pituitary suppression, and (d) low thyroid hormone function associated with redox imbalance in ME/CFS. Moreover, we describe interlinkages between these pathophysiological mechanisms as well as “vicious cycles” involving cytokines and inflammation that may contribute to explain the chronic nature of these illnesses. This paper summarizes and expands on our previous publications about the relevance of findings from critical illness for ME/CFS. New knowledge on diagnostics, prognostics and treatment strategies could be gained through active collaboration between critical illness and ME/CFS researchers, which could lead to improved outcomes for both conditions.

Sparse System Identification of Leptin Dynamics in Women With Obesity

The prevalence of obesity is increasing around the world at an alarming rate. The interplay of the hormone leptin with the hypothalamus-pituitary-adrenal axis plays an important role in regulating energy balance, thereby contributing to obesity. This study presents a mathematical model, which describes hormonal behavior leading to an energy abnormal equilibrium that contributes to obesity. To this end, we analyze the behavior of two neuroendocrine hormones, leptin and cortisol, in a cohort of women with obesity, with simplified minimal state-space modeling. Using a system theoretic approach, coordinate descent method, and sparse recovery, we deconvolved the serum leptin-cortisol levels. Accordingly, we estimate the secretion patterns, timings, amplitudes, number of underlying pulses, infusion, and clearance rates of hormones in eighteen premenopausal women with obesity. Our results show that minimal state-space model was able to successfully capture the leptin and cortisol sparse dynamics with the multiple correlation coefficients greater than 0.83 and 0.87, respectively. Furthermore, the Granger causality test demonstrated a negative prospective predictive relationship between leptin and cortisol, 14 of 18 women. These results indicate that increases in cortisol are prospectively associated with reductions in leptin and vice versa, suggesting a bidirectional negative inhibitory relationship. As dysregulation of leptin may result in an abnormality in satiety and thereby associated to obesity, the investigation of leptin-cortisol sparse dynamics may offer a better diagnostic methodology to improve better treatments plans for individuals with obesity.

Prevalence of fibromyalgia in medical students and its association with lifestyle factors - a cross-sectional study

Objectives

Fibromyalgia (FM) is a chronic widespread pain syndrome, known to be associated with several other symptoms. Chronic stress is suspected to be a contributing factor in the pathogenesis of FM. It is known that medical students are under a constant state of stress originating from personal and social expectations. The aim of the study was to assess the prevalence of FM in this population and identify lifestyle parameters influencing FM severity.

Material and methods

An online survey of first- and final-year medical students was conducted using the ACR modified 2016 criteria and FANTASTIC checklist. The survey acquired demographic information such as age, gender, year, and division of studies. A subgroup analysis based on gender, year of studies, and division of studies was performed.

Results

439 medical students (71% females) completed the survey. The overall prevalence of FM in our cohort was 10.48%. The ratio of females to males was 3 : 1. A significant negative correlation between better quality of lifestyle and worse FM severity was observed in all subgroups. The “insight”, “sleep and stress”, “behavior” and “career” domains of lifestyle were found to have a significant negative correlation with FM severity on univariate analysis.

Conclusions

The prevalence of FM in medical students seems to be considerably higher than in the general population. Chronic stress levels, sleep problems, social support, and behavior seem to be the major factors influencing FM severity in this population. Our findings suggest that medical students must be considered a “high-risk” group for FM, and hence must be identified, educated, and managed accordingly. It is, therefore, important for medical universities to implement programs educating students about FM, the importance of a healthy lifestyle, and stress coping strategies, while also making systemic changes to curb stressors in medical training.

Closed-Loop Fuzzy Energy Regulation in Patients With Hypercortisolism via Inhibitory and Excitatory Intermittent Actuation

Hypercortisolism or Cushing's disease, which corresponds to the excessive levels of cortisol hormone, is associated with tiredness and fatigue during the day and disturbed sleep at night. Our goal is to employ a wearable brain machine interface architecture to regulate one's energy levels in hypercortisolism. In the present simulation study, we generate multi-day cortisol profile data for ten subjects both in healthy and disease conditions. To relate an internal hidden cognitive energy state to one's cortisol secretion patterns, we employ a state-space model. Particularly, we consider circadian upper and lower bound envelopes on cortisol levels, and timings of hypothalamic pulsatile activity underlying cortisol secretions as continuous and binary observations, respectively. To estimate the hidden cognitive energy-related state, we use Bayesian filtering. In our proposed architecture, we infer one's cognitive energy-related state using wearable devices rather than monitoring the brain activity directly and close the loop utilizing fuzzy control. To model actuation in the real-time closed-loop architecture, we simulate two types of medications that result in increasing and decreasing the energy levels in the body. Finally, we close the loop using a knowledge-based control approach. The results on ten simulated profiles verify how the proposed architecture is able to track the energy state and regulate it using hypothetical medications. In a simulation study based on experimental data, we illustrate the feasibility of designing a wearable brain machine interface architecture for energy regulation in hypercortisolism. This simulation study is a first step toward the ultimate goal of managing hypercortisolism in real-world situations.

Hypothesis: Mechanisms That Prevent Recovery in Prolonged ICU Patients Also Underlie Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Here the hypothesis is advanced that maladaptive mechanisms that prevent recovery in some intensive care unit (ICU) patients may also underlie Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Specifically, these mechanisms are: (a) suppression of the pituitary gland's pulsatile secretion of tropic hormones, and (b) a "vicious circle" between inflammation, oxidative and nitrosative stress (O&NS), and low thyroid hormone function. This hypothesis should be investigated through collaborative research projects.

Identification of Sympathetic Nervous System Activation From Skin Conductance: A Sparse Decomposition Approach With Physiological Priors

OBJECTIVE

Sweat secretions lead to variations in skin conductance (SC) signal. The relatively fast variation of SC, called the phasic component, reflects sympathetic nervous system activity. The slow variation related to thermoregulation and general arousal is known as the tonic component. It is challenging to decompose the SC signal into its constituents to decipher the encoded neural information related to emotional arousal.

METHODS

We model the phasic component using a second-order differential equation representing the diffusion and evaporation processes of sweating. We include a sparse impulsive neural signal that stimulates the sweat glands for sweat production. We model the tonic component with several cubic B-spline functions. We formulate an optimization problem with physiological priors on system parameters, a sparsity prior on the neural stimuli, and a smoothness prior on the tonic component. Finally, we employ a generalized-cross-validation-based coordinate descent approach to balance among the smoothness of the tonic component, the sparsity of the neural stimuli, and the residual.

RESULTS

We illustrate that we can successfully recover the unknowns separating both tonic and phasic components from both experimental and simulated data (with ). Further, we successfully demonstrate our ability to automatically identify the sparsity level for the neural stimuli and the smoothness level for the tonic component.

CONCLUSION

Our generalized-cross-validation-based novel method for SC signal decomposition successfully addresses previous challenges and retrieves a physiologically plausible solution.

SIGNIFICANCE

Accurate decomposition of SC could potentially improve cognitive stress tracking in patients with mental disorders.