BayesiaLab
Exploratory and Confirmatory Bayesian Networks

Exploratory and Confirmatory Bayesian Networks

Presented at the 10th Annual BayesiaLab Conference on Wednesday, October 25, 2022.

Abstract

Depression is a highly heterogeneous mental health concern, making it difficult to determine optimal treatment approaches. Mindfulness-based interventions have been shown in meta-analyses to be moderately effective in reducing symptoms of depression. Research identifying mechanisms governing the efficacy of mindfulness-based interventions might be guided by analytic approaches that can generate hypotheses to test in future research. Network analysis (often utilizing Gaussian Graphical Models) is an item-level approach widely used in recent psychological research to understand interrelations within and between heterogeneous mental health constructs.

To identify the interrelations between symptoms of depression and features of mindfulness, we used Bayesian Network Analysis across three cross-sectional samples (N = 1,135). Bayesian Gaussian Graphical Models allowed us to (1) generate an exploratory network in two samples using different depression assessments: the Patient Health Questionnaire (n = 384) and the Depression Anxiety and Stress Scale (n = 350), with mindfulness being assessed using the Five-Facet Mindfulness Scale and (2) confirm findings from the exploratory network in a third sample (n = 401) with a pre-registered replication.

From the exploratory analyses, we found that the Non-judging facet of mindfulness (reflecting acceptance of thoughts and feelings) was the most central (i.e., interconnected) bridge to symptoms of depression. The pre-registered analysis confirmed our initial findings: after controlling for all other associations, Non-judging represented the most central connection between facets of mindfulness and depression. These results suggest that when considering the use of mindfulness-based interventions for individuals with depression, examination of Non-judging is warranted and may offer a potent target.

Presentation Video

Presentation Slides

About the Presenter

Mikael Rubin is an Assistant Professor at Palo Alto University. He received his Ph.D. in clinical psychology from the University of Texas at Austin. From studying virtual reality in art to conducting virtual reality exposure therapy, he is curious about how what we attend to influences how we make meaning out of the lived experience. He specializes in research and interventions related to anxiety and post-traumatic stress. His research has used a wide range of approaches (including eye tracking, neuroimaging, and network analysis). He directs the Transdiagnostic Attention Intervention (TRAIN) Lab at Palo Alto University and is especially interested in using virtual reality and eye-tracking methods to evaluate, enhance, and widely disseminate mental health interventions.


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