Examining attentional functioning in depression using a personalized network approach: A proof-of-principle study

Abstract

Reduced attentional functioning has been identified as an important factor in depression etiology and maintenance. However, current research does not fully take into account the large heterogeneity of depression, for example identifying for whom and how reduced attentional functioning plays a role. In this proof-of-principle study, we demonstrate how a personalized network approach can provide more nuanced insight into the role of attentional functioning in depression. To this end, we estimated person-specific symptom networks in a depression sample, and explored associations between reduced attentional functioning (alerting, orienting, executive control) and symptom centrality (expected influence). Participants with ongoing and remitted depression were enrolled to 14 days of intensive assessment of depression symptoms in their daily life using a smartphone app. Based on these data, person-specific network models were estimated using vector autoregression modelling. Orienting, alerting and executive control were assessed using the Attentional Network Test in the laboratory. Person-specific networks showed large variability in symptom dynamics. Higher centrality of fatigue was associated with reduced orienting efficiency, and higher centrality of passivity was associated with reduced executive control. This study highlights the potential of assessing individual symptom dynamics when considering cognitive functioning in depression.

Keywords

• depression
• Attention
• Experience sampling methods
• Ecological momentary assessment
• Fatigue
• Passivity