Brain rhythms of pain

Pain is an integrative phenomenon that results from dynamic interactions between sensory and contextual (i.e., cognitive, emotional, and motivational) processes. In the brain the experience of pain is associated with neuronal oscillations and synchrony at different frequencies. However, an overarching framework for the significance of oscillations for pain remains lacking. Recent concepts relate oscillations at different frequencies to the routing of information flow in the brain and the signaling of predictions and prediction errors. The application of these concepts to pain promises insights into how flexible routing of information flow coordinates diverse processes that merge into the experience of pain. Such insights might have implications for the understanding and treatment of chronic pain

Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach.

Recent task fMRI studies suggest that individual differences in trait empathy and empathic concern are mediated by patterns of connectivity between self-other resonance and top-down control networks that are stable across task demands. An untested implication of this hypothesis is that these stable patterns of connectivity should be visible even in the absence of empathy tasks. Using machine learning, we demonstrate that patterns of resting state fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of explicit task demands) of resonance and control networks predict trait empathic concern (n = 58). Empathic concern was also predicted by connectivity patterns within the somatomotor network. These findings further support the role of resonance-control network interactions and of somatomotor function in our vicariously driven concern for others. Furthermore, a practical implication of these results is that it is possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments

The reliability and validity of functional brain connectivity compared to a self-reported measure of pain

Pain is a multidimensional perception that is complex in nature. It is a unitary construct that includes overlapping domains such as intensity, affect, quality, and frequency. These domains do not reflect the amount of tissue damage. It reflects the end result of the perception of pain in which multiple biopsychosocial factors are involved (Gatchel et al., 2007). Multiple self-reported measures have been used in an attempt to capture most factors that may influence pain such as psychological factors. However, there is no one scale that can be used to characterize pain as a whole with all its factors. Furthermore, physical measurements did not prove to be better than self-reported measure in pain characterization. Since pain perception is believed to occur in the brain, it seems rational to measure aspects of the brain as a biomarker for pain. One method that has been recently used is functional connectivity magnetic resonance imaging (fcMRI), which is a measure of the connectivity between brain regions that are previously known to be related to pain. In this paper the focus will be on the recent “physical measure” of pain in comparison to the self-reported measure, the Gracely box scale. First a summary of the reliability and validity of the Gracely box scale will be mentioned. Then the development of the functional connectivity based on the fMRI studies will be addressed. Finally, I will assess the reliability and validity of the measure compared to the Gracely box scale

An interoceptive predictive coding model of conscious presence

We describe a theoretical model of the neurocognitive mechanisms underlying conscious presence and its disturbances. The model is based on interoceptive prediction error and is informed by predictive models of agency, general models of hierarchical predictive coding and dopaminergic signaling in cortex, the role of the anterior insular cortex (AIC) in interoception and emotion, and cognitive neuroscience evidence from studies of virtual reality and of psychiatric disorders of presence, specifically depersonalization/derealization disorder. The model associates presence with successful suppression by top-down predictions of informative interoceptive signals evoked by autonomic control signals and, indirectly, by visceral responses to afferent sensory signals. The model connects presence to agency by allowing that predicted interoceptive signals will depend on whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused. Anatomically, we identify the AIC as the likely locus of key neural comparator mechanisms. Our model integrates a broad range of previously disparate evidence, makes predictions for conjoint manipulations of agency and presence, offers a new view of emotion as interoceptive inference, and represents a step toward a mechanistic account of a fundamental phenomenological property of consciousness

Robot Models of Mental Disorders

Alongside technological tools to support wellbeing and treatment of mental disorders, models of these disorders can also be invaluable tools to understand, support and improve these conditions. Robots can provide ecologically valid models that take into account embodiment-, interaction-, and context-related elements. Focusing on Obsessive-Compulsive spectrum disorders, in this paper we discuss some of the potential contributions of robot models and relate them to other models used in psychology and psychiatry, particularly animal models. We also present some initial recommendations for their meaningful design and rigorous use.Final Accepted Versio

Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain

Nearly a quarter of visits to the Emergency Department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of big data sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease community. Sickle cell disease is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). Pilot testing of our approach suggests that it has significant potential to predict pain dynamics given patients' reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data driven recommendations for treating chronic pain.Comment: 13 pages, 15 figures, 5 table