期刊
JOURNAL OF ANXIETY DISORDERS
卷 42, 期 -, 页码 95-104出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.janxdis.2016.06.009
关键词
Network analysis; Graph theory; Attentional bias for threat; Attention networks; Social anxiety disorder; Community detection; Computational social sciences
资金
- Grant (F.R.S.-FNRS) from the Belgian National Fund for Scientific Research [FC78142]
- Belgian Foundation for Vocation (Vocatio)
- WBI World Excellence Grant [sub/2015/228106243177]
Cognitive models posit that social anxiety disorder (SAD) is associated with and maintained by biased attention allocation vis-a-vis social threat. However, over the last decade, there has been intense debate regarding whether AB in SAD results from preferential engagement with or difficulty in disengaging from social threat. Further, recent evidence suggests that AB may merely result from top-down attentional impairments vis-a-vis non-emotional material. Consequently, uncertainty still abounds regarding both the relative importance and the mutual interactions of these different processes and SAD symptoms. Inspired by novel network approaches to psychopathology that conceptualize symptoms as complex dynamic systems of mutually interacting variables, we computed weighted directed networks to investigate potential causal relations among laboratory measures of attentional components and symptoms of social anxiety disorder. Global and local connectivity of network structures revealed that the three most central variables were the orienting component of attention as well as both avoidance and fear of social situations. Neither preferential attention engagement with threat nor difficulty disengaging from threat exhibited high relative importance as predictors of symptoms in the network. Together, these findings suggest the value of extending the network approach beyond self-reported clinical symptoms to incorporate process-level measures from laboratory tasks to gain new insight into the mechanisms of SAD. (C) 2016 Elsevier Ltd. All rights reserved.
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