Microglia are emerging as critical regulators of neuronal function and behavior in nearly every area of neuroscience. Initial reports focused on classical immune functions of microglia in pathological contexts, however, immunological concepts from these studies have been applied to describe neuro-immune interactions in the absence of disease, injury, or infection. Indeed, terms such as 'microglia activation' or 'neuroinflammation' are used ubiquitously to describe changes in neuro-immune function in disparate contexts; particularly in stress research, where these terms prompt undue comparisons to pathological conditions. This creates a barrier for investigators new to neuro-immunology and ultimately hinders our understanding of stress effects on microglia. As more studies seek to understand the role of microglia in neurobiology and behavior, it is increasingly important to develop standard methods to study and define microglial phenotype and function. In this review, we summarize primary research on the role of microglia in pathological and physiological contexts. Further, we propose a framework to better describe changes in microglia1 phenotype and function in chronic stress. This approach will enable more precise characterization of microglia in different contexts, which should facilitate development of microglia-directed therapeutics in psychiatric and neurological disease.

The semantics of microglia activation: neuroinflammation, homeostasis, and stress.

A new science of mental disorders: Using personalised, transdiagnostic, dynamical systems to understand, model, diagnose and treat psychopathology.

Intervening on psychopathology networks: Evaluating intervention targets through simulations.

Background: Depressive symptoms can affect people’s quality of life and social environment. In addition, in severe situations, they can lead to suicidal behaviors. Objective: This study aimed to analyze the differences in depressive symptoms in underweight and obese Peruvian adults. Methods: A cross-sectional study was carried out based on secondary data obtained from the Instituto Nacional de Estadística e Informática (INEI), Lima, Peru. A sample of 10 053 participants was considered, of which 55.96% were women. Two Gaussian plot models were estimated and the levels of depressive symptomatology were compared between the 2 groups (adults with underweight and obese). Results: A total of 1510 (15.02%) were underweight adults and 8543 (84.98%) were obese adults. There were differences in the reporting of depressive symptoms in the underweight group; the most central items were “Depressed mood” (PH2), “Tiredness/low energy” (PH4), and “Psychomotor difficulties” (PH8). Conclusion: This study provides new evidence on the dynamic relationship between depressive symptoms according to the body mass index categories (underweight and obese) assessed.

https://journals.sagepub.com/doi/pdf/10.1177/21501319221096917

Network Analysis of Depressive Symptomatology in Underweight and Obese Adults

Clark and Wells’ prominent model of social anxiety disorder (SAD) assumes that cognitive variables such as negative expectations or dysfunctional cognitions play a central role in the symptomatology of SAD. In contrast to adults, it is less clear how well the cognitive model can be applied to children and adolescents. A network analysis with seven nodes was conducted to explore the importance of cognitive variables and their interaction with symptoms of SAD based on N = 205 children and adolescents (8–18 years, M = 11.54 years). Cognitive variables had a high but differential impact within the positively connected network of SAD. Dysfunctional cognitions were most strongly connected within the network. Dysfunctional cognitions, as predicted by Clark and Wells’ model, seem to act as a hub affecting several symptoms. The association between negative expectations and avoidance indicates that negative expectations may particularly contribute to the maintenance of SAD.

/pdf/cognitive-variables-in-social-anxiety-disorder-in-children-2eji4yhzxm.pdf

Cognitive Variables in Social Anxiety Disorder in Children and Adolescents: A Network Analysis.

Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled towards a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system’s healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.

/pdf/the-mental-health-ecosystem-extending-symptom-networks-with-1fivfzv38g.pdf

The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors.

Cognition played a pivotal role in the acceleration of technological innovation during the Industrial Revolution. Growing affluence may have provided favourable environmental conditions for a boost in cognition, enabling individuals to tackle more complex (industrial) problems. Dynamical systems thinking may provide useful tools to describe sudden transitions like the Industrial Revolution, by modelling the recursive feedback between psychology and environment.

Affluence boosted intelligence? How the interaction between cognition and environment may have produced an eighteenth-century Flynn effect during the Industrial Revolution

Max A. van der Linden

Papers

The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors.

Affluence boosted intelligence? How the interaction between cognition and environment may have produced an eighteenth-century Flynn effect during the Industrial Revolution