Network analysis of depression and anxiety symptom relationships in a psychiatric sample.

BACKGROUND: Researchers have studied psychological disorders extensively from a common cause perspective, in which symptoms are treated as independent indicators of an underlying disease. In contrast, the causal systems perspective seeks to understand the importance of individual symptoms and symptom-to-symptom relationships. In the current study, we used network analysis to examine the relationships between and among depression and anxiety symptoms from the causal systems perspective. METHOD: We utilized data from a large psychiatric sample at admission and discharge from a partial hospital program (N = 1029, mean treatment duration = 8 days). We investigated features of the depression/anxiety network including topology, network centrality, stability of the network at admission and discharge, as well as change in the network over the course of treatment. RESULTS: Individual symptoms of depression and anxiety were more related to other symptoms within each disorder than to symptoms between disorders. Sad mood and worry were among the most central symptoms in the network. The network structure was stable both at admission and between admission and discharge, although the overall strength of symptom relationships increased as symptom severity decreased over the course of treatment. CONCLUSIONS: Examining depression and anxiety symptoms as dynamic systems may provide novel insights into the maintenance of these mental health problems.

Isolating biomarkers for symptomatic states: considering symptom-substrate chronometry.

A long-standing goal of psychopathology research is to develop objective markers of symptomatic states, yet progress has been far slower than expected. Although prior reviews have attributed this state of affairs to diagnostic heterogeneity, symptom comorbidity and phenotypic complexity, little attention has been paid to the implications of intra-individual symptom dynamics and inter-relatedness for biomarker study designs. In this critical review, we consider the impact of short-term symptom fluctuations on widely used study designs that regress the 'average level' of a given symptom against biological data collected at a single time point, and summarize findings from ambulatory assessment studies suggesting that such designs may be sub-optimal to detect symptom-substrate relationships. Although such designs have a crucial role in advancing our understanding of biological substrates related to more stable, longer-term changes (for example, gray matter thinning during a depressive episode), they may be less optimal for the detection of symptoms that exhibit high frequency fluctuations, are susceptible to common reporting biases, or may be heavily influenced by the presence of other symptoms. We propose that a greater emphasis on intra-individual symptom chronometry may be useful for identifying subgroups of patients with common, proximal pathological indicators. Taken together, these three recent developments in the areas of symptom conceptualization and measurement raise important considerations for future studies attempting to identify reliable biomarkers in psychiatry.

Patient attitudes toward nursing interventions.

When attitudes toward nursing interventions of 103 psychiatric inpatients studied over a 28-month period, in three different ward phases, were correlated with demographic, psychiatric, and psychologic test variables, attitudes varied premirily and almost exclusively with ward administrative phase. Within the differing ward phases the nursing interventions most valued by patients did not focus directly upon the individual patient.