The Continuum Between Temperament and Mental Illness as Dynamical Phases and Transitions

Chronic pain as an emergent property of a complex system and the potential roles of psychedelic therapies

Despite research advances and urgent calls by national and global health organizations, clinical outcomes for millions of people suffering with chronic pain remain poor. We suggest bringing the lens of complexity science to this problem, conceptualizing chronic pain as an emergent property of a complex biopsychosocial system. We frame pain-related physiology, neuroscience, developmental psychology, learning, and epigenetics as components and mini-systems that interact together and with changing socioenvironmental conditions, as an overarching complex system that gives rise to the emergent phenomenon of chronic pain. We postulate that the behavior of complex systems may help to explain persistence of chronic pain despite current treatments. From this perspective, chronic pain may benefit from therapies that can be both disruptive and adaptive at higher orders within the complex system. We explore psychedelic-assisted therapies and how these may overlap with and complement mindfulness-based approaches to this end. Both mindfulness and psychedelic therapies have been shown to have transdiagnostic value, due in part to disruptive effects on rigid cognitive, emotional, and behavioral patterns as well their ability to promote neuroplasticity. Psychedelic therapies may hold unique promise for the management of chronic pain.

A head-to-head comparison of eight unique personality systems in predicting somatization phenomenon

BACKGROUND

If somatization is an independent personality trait, it is not clear whether it is specific to the temperament or maladaptive spectrum of personality. We aimed at the head-to-head comparison of temperament and maladaptive systems and spectra of personality to predict both somatization and somatic symptom and related disorders (SSRD).

METHODS

The samples included 257 cases with SSRD (70.8% female) and 1007 non-SSRD (64.3% female) from Western Iran. The Personality Inventory for DSM-5 (PID-5), Personality Diagnostic Questionnaire-4 (PDQ-4), Temperament and Character Inventory (TCI), Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Autoquestionnaire (TEMPS-A), Affective and Emotional Composite Temperament Scale (AFECTS), and Positive Affect and Negative Affect Model (PANAS) was used to data collection. A somatization factor plus temperament and maladaptive spectra of personality were extracted using exploratory factor analysis. Several hierarchical linear and logistic regressions were used to test the predictive systems and spectra.

RESULTS

All personality systems jointly predict both somatization and SSRD with a slightly higher contribution for temperament systems. When the temperament and maladaptive spectra were compared, both spectra above each other significantly predicted both somatization (R2 = .407 versus .263) and SSRD (R2 = .280 versus .211). The temperament spectrum explained more variance beyond the maladaptive spectrum when predicting both the somatization factor (change in R2 = .156 versus .012) and SSRD (change in R2 = .079 versus .010).

CONCLUSION

All temperament and maladaptive frameworks of personality are complementary to predicting both somatization and SSRD. However, the somatization is more related to the temperament than the maladaptive spectrum of personality.

Dynamical systems in computational psychiatry: A toy-model to apprehend the dynamics of psychiatric symptoms

Introduction

These last years, scientific research focuses on the dynamical aspects of psychiatric disorders and their clinical significance. In this article, we proposed a theoretical framework formalized as a generic mathematical model capturing the heterogeneous individual evolutions of psychiatric symptoms. The first goal of this computational model based on differential equations is to illustrate the nonlinear dynamics of psychiatric symptoms. It offers an original approach to nonlinear dynamics to clinical psychiatrists.

Methods

In this study, we propose a 3+1 dimensions model (x, y, z + f) reproducing the clinical observations encountered in clinical psychiatry with: a variable modeling environmental noise (z) on the patient's internal factors (y) with its temporal specificities (f) and symptomatology (x). This toy-model is able to integrate empirical or simulated data from the influence of perceived environmental over time, their potential importance on the internal and subjective patient-specific elements, and their interaction with the apparent intensity of symptoms.

Results

Constrained by clinical observation of case formulations, the dynamics of psychiatric symptoms is studied through four main psychiatric conditions were modeled: i) a healthy situation, ii) a kind of psychiatric disorder evolving following an outbreak (i.e., schizophrenia spectrum), iii) a kind of psychiatric disorder evolving by kindling and bursts (e.g., bipolar and related disorders); iv) and a kind of psychiatric disorder evolving due to its high susceptibility to the environment (e.g., spersistent complex bereavement disorder). Moreover, we simulate the action of treatments on different psychiatric conditions.

Discussion

We show that the challenges of dynamical systems allow to understand the interactions of psychiatric symptoms with environmental, descriptive, subjective or biological variables. Although this non-linear dynamical model has limitations (e.g., explanatory scope or discriminant validity), simulations provide at least five main interests for clinical psychiatry, such as a visualization of the potential different evolution of psychiatric disorders, formulation of clinical cases, information about attracting states and bifurcations, or the possibility of a nosological refinement of psychiatric models (e.g., staging and symptom network models).