Emotional and Cognitive Consequences of Social Rejection: An Entry Door to Major Depression

Interpersonal factors are strong predictors of the onset and course of major depression. However, the biological and neural bases of interpersonal difficulties in major depression are unknown. In this chapter we describe a general homeostatic system that monitors the social acceptance of individuals. We show that this system is activated in response to actual or putative threats to social acceptance and signals of social rejection. Our model describes a cascade of cognitive, emotional, and behavioural consequences of social exclusion. The model emphasizes the role of specific regions—the subgenual anterior cingulate, the insula, and the default mode network—in the detection and regulation of social signals. Hence we propose that major depressive disorder is tightly linked to the processing of social exclusion and may represent a specific impairment in the homeostatic system that monitors social acceptance.

Immunopsychiatry

In recent years, great attention has been devoted to the understanding of the immune dysfunction that is associated with major psychiatric disorders. In the context of the reconceptualization of the immune system as a homeostatic system, immune cells, molecules and mechanisms are highly promising as new diagnostic and therapeutic targets to reduce the burden of mental/psychiatric disorders. In this regard, immune cells, molecules and mechanisms are highly promising. The literature on immunology of psychiatric disorders is still disperse, and only very few attempts have been done so far to consolidate the current knowledge in this expanding and exciting area. Each chapter will present the available data on the immune/inflammatory dysfunction in psychiatric disorders, indicating the potential use of novel immunological biomarkers or therapeutic targets, as well as discussing the challenges ahead to incorporate this knowledge into the clinical practice.

Inferring causal pathways among three or more variables from steady-state correlations in a homeostatic system

Cross-sectional correlations between two variables have limited implications for causality. We show here that in a homeostatic system with three or more inter-correlated variables, it is possible to make causal inferences from steady-state data. Every putative pathway between three variables makes a set of differential predictions that can be tested with steady state data. For example, among 3 variables, A, B and C, the coefficient of determination, is predicted by the product of and for some pathways, but not for others. Residuals from a regression line are independent of residuals from another regression for some pathways, but positively or negatively correlated for certain other pathways. Different pathways therefore have different prediction signatures, which can be used to accept or reject plausible pathways. We apply these principles to test the classical pathway leading to a hyperinsulinemic normoglycemic insulin-resistant, or pre-diabetic state using four different sets of epidemiological data. Currently, a set of indices called HOMA-IR and HOMA-β are used to represent insulin resistance and glucose-stimulated insulin response by β cells respectively. Our analysis shows that if we assume the HOMA indices to be faithful indicators, the classical pathway must in turn, be rejected. Among the populations sampled, the classical pathway and faithfulness of the HOMA indices cannot be simultaneously true. The principles and tools described here can find wide application in inferring plausible regulatory mechanisms in homeostatic systems based on epidemiological data.