Disparities across Diverse Populations in the Health and Treatment of Patients with Osteoarthritis.

The study of disparities across diverse populations regarding the health and treatment of patients with osteoarthritis (OA) is recognized as a priority for investigation and action by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) and the American Academy of Orthopedic Surgeons (AAOS). OA is a common condition that increases with age, but with prevalence generally similar across racial and ethnic groups. However, disparities in the treatment of OA among racial, ethnic, and socioeconomic groups are well-documented and continue to rise and persist. The reasons are complex, likely involving a combination of patient, provider, and healthcare system factors. Treatment disparities among these different populations have an impact on clinical outcomes, healthcare, and productivity, and are projected to increase significantly with the growing diversity of the United States population. The aim of this short review is to summarize studies of racial, ethnic, and socioeconomic disparities among patients with OA in the United States, with a focus on prevalence, treatment utilization, and clinical and economic outcomes.

Broadband slow-wave modulation in posterior and anterior cortex tracks distinct states of propofol-induced unconsciousness.

A controversy has developed in recent years over the roles of frontal and posterior cortices in mediating consciousness and unconsciousness. Disruption of posterior cortex during sleep appears to suppress the contents of dreaming, yet activation of frontal cortex appears necessary for perception and can reverse unconsciousness under anesthesia. We used anesthesia to study how regional cortical disruption, mediated by slow wave modulation of broadband activity, changes during unconsciousness in humans. We found that broadband slow-wave modulation enveloped posterior cortex when subjects initially became unconscious, but later encompassed both frontal and posterior cortex when subjects were more deeply anesthetized and likely unarousable. Our results suggest that unconsciousness under anesthesia comprises several distinct shifts in brain state that disrupt the contents of consciousness distinct from arousal and awareness of those contents.

Statistical modeling of behavioral dynamics during propofol-induced loss of consciousness.

BACKGROUND: Accurate quantitative analysis of the changes in responses to external stimuli is crucial for characterizing the timing of loss and recovery of consciousness induced by anesthetic drugs. We studied induction and emergence from unconsciousness achieved by administering a computer-controlled infusion of propofol to ten human volunteers. We evaluated loss and recovery of consciousness by having subjects execute every 4s two interleaved computer delivered behavioral tasks: responding to verbal stimuli (neutral words or the subject's name), or less salient stimuli of auditory clicks. NEW METHOD: We analyzed the data using state-space methods. For each stimulus type the observation model is a two-stage binomial model and the state model is two dimensional random walk in which one cognitive state governs the probability of responding and the second governs the probability of correctly responding given a response. We fit the model to the experimental data using Bayesian Monte Carlo methods. RESULTS: During induction subjects lost responsiveness to less salient clicks before losing responsiveness to the more salient verbal stimuli. During emergence subjects regained responsiveness to the more salient verbal stimuli before regaining responsiveness to the less salient clicks. COMPARISON WITH EXISTING METHOD(S): The current state-space model is an extension of previous model used to analyze learning and behavioral performance. In this study, the probability of responding on each trial is obtained separately from the probability of behavioral performance. CONCLUSIONS: Our analysis provides a principled quantitative approach for defining loss and recovery of consciousness in experimental studies of general anesthesia.

A transition in brain state during propofol-induced unconsciousness.

Rhythmic oscillations shape cortical dynamics during active behavior, sleep, and general anesthesia. Cross-frequency phase-amplitude coupling is a prominent feature of cortical oscillations, but its role in organizing conscious and unconscious brain states is poorly understood. Using high-density EEG and intracranial electrocorticography during gradual induction of propofol general anesthesia in humans, we discovered a rapid drug-induced transition between distinct states with opposite phase-amplitude coupling and different cortical source distributions. One state occurs during unconsciousness and may be similar to sleep slow oscillations. A second state occurs at the loss or recovery of consciousness and resembles an enhanced slow cortical potential. These results provide objective electrophysiological landmarks of distinct unconscious brain states, and could be used to help improve EEG-based monitoring for general anesthesia.

Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia.

Accurate quantification of loss of response to external stimuli is essential for understanding the mechanisms of loss of consciousness under general anesthesia. We present a new approach for quantifying three possible outcomes that are encountered in behavioral experiments during general anesthesia: correct responses, incorrect responses and no response. We use a state-space model with two state variables representing a probability of response and a conditional probability of correct response. We show applications of this approach to an example of responses to auditory stimuli at varying levels of propofol anesthesia ranging from light sedation to deep anesthesia in human subjects. The posterior probability densities of model parameters and the response probability are computed within a Bayesian framework using Markov Chain Monte Carlo methods.

Robust time-varying multivariate coherence estimation: application to electroencephalogram recordings during general anesthesia.

Coherence analysis characterizes frequency-dependent covariance between signals, and is useful for multivariate oscillatory data often encountered in neuroscience. The global coherence provides a summary of coherent behavior in high-dimensional multivariate data by quantifying the concentration of variance in the first mode of an eigenvalue decomposition of the cross-spectral matrix. Practical application of this useful method is sensitive to noise, and can confound coherent activity in disparate neural populations or spatial locations that have a similar frequency structure. In this paper we describe two methodological enhancements to the global coherence procedure that increase robustness of the technique to noise, and that allow characterization of how power within specific coherent modes change through time.

Simultaneous electroencephalography and functional magnetic resonance imaging of general anesthesia.

It has been long appreciated that anesthetic drugs induce stereotyped changes in electroencephalogram (EEG), but the relationships between the EEG and underlying brain function remain poorly understood. Functional imaging methods including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), have become important tools for studying how anesthetic drugs act in the human brain to induce the state of general anesthesia. To date, no investigation has combined functional MRI with EEG to study general anesthesia. We report here a paradigm for conducting combined fMRI and EEG studies of human subjects under general anesthesia. We discuss the several technical and safety problems that must be solved to undertake this type of multimodal functional imaging and show combined recordings from a human subject. Combined fMRI and EEG exploits simultaneously the high spatial resolution of fMRI and the high temporal resolution of EEG. In addition, combined fMRI and EEG offers a direct way to relate established EEG patterns induced by general anesthesia to changes in neural activity in specific brain regions as measured by changes in fMRI blood oxygen level dependent (BOLD) signals.