Background: Comorbidity between depressive and anxiety disorders is common. From network perspective, mental disorders arise from direct interactions between symptoms and comorbidity is due to direct interactions between depression and anxiety symptoms. The current study investigates the network structure of depression and anxiety symptoms in Chinese female nursing students and identifies the central and bridge symptoms as well as how other symptoms in present network are related to depression symptom “thoughts of death”. Methods: To understand the full spectrum of depression and anxiety, we recruited 776 Chinese female nursing students with symptoms of depression and anxiety that span the full range of normal to abnormal. Depression symptoms were measured by Patient Health Questionnaire-9 while anxiety symptoms were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Network analysis was used to construct networks. Specifically, we computed the predictability, expected influence and bridge expected influence for each symptom and showed a flow network of “thoughts of death”. Results: Nine strongest edges existed in network were from the same disorder. Four were between depression symptoms, like “sleep difficulties” and “fatigue”, and “anhedonia” and “fatigue”. Five were between anxiety symptoms, like “nervousness or anxiety” and “worry too much”, and “restlessness” and “afraid something will happen”. The symptom “fatigue”, “feeling of worthlessness” and “irritable” had the highest expected influence centrality. Results also revealed two bridge symptoms: “depressed or sad mood” and “irritable”. As to “thoughts of death”, the direct relations between it and “psychomotor agitation/retardation” and “feeling of worthlessness” were the strongest direct relations. Conclusions: The current study highlighted critical central symptoms “fatigue”, “feeling of worthlessness” and “irritable” and critical bridge symptoms “depressed or sad mood” and “irritable”. Particularly, “psychomotor agitation/retardation” and “feeling of worthlessness” were identified as key priorities due to their strongest associations with suicide ideation. Implications for clinical prevention and intervention based on these symptoms are discussed.

Existing research proposed that moving from a disorder-level analysis to a symptom-level analysis may provide a more fine-grained understanding of psychopathology. This study aimed to explore the relations between two dimensions (i.e., cognitive reappraisal, CR; expressive suppression, ES) of emotion regulation and individual symptoms of depression and anxiety among medical staff during the late stage of COVID-19 pandemic. We examined depression symptoms, anxiety symptoms and emotion regulation among 420 medical staff during the late stage of COVID-19 pandemic via network analysis. Two networks (i.e. emotion regulation-depression network and emotion regulation-anxiety network) were constructed in the present study. Bridge centrality index was calculated for each variable within the two networks. Among the present sample, the prevalences of depression and anxiety are 39.5% and 26.0%. CR and ES showed distinct connections to symptoms of depression and anxiety. Results of bridge centrality showed that in both networks, CR had a negative bridge expected influence value while ES had a positive bridge expected influence value. The results revealed the specific role of CR and ES in relation to depression and anxiety at a symptom level. Implications for clinical preventions and interventions are discussed.

Background: Problematic smartphone use (PSU) has become an issue of great concern in the age of smartphones. PSU is associated with emotional problems, one of which is depression, as shown by empirical studies. However, previous studies have been limited in that they have focused solely on the total score for symptoms of PSU and depression while ignoring the symptomatic heterogeneity of these two concepts.Method: This study filled this gap by exploring links between symptoms of PSU and depression among 1,849 university students (59.17% female participants, 17–23 years old). Network analysis was utilized to reveal symptom connections, central symptoms, and bridge symptoms between PSU and depression.Results: (1) A total of 17 of 81 symptom connections (about 21%) between PSU and depression existed in the symptom network. For example, “self-control failure” for PSU was positively correlated with “concentration problems” for depression; (2) “recklessly continuing” for PSU and “fatigue” for depression were central symptoms within the PSU symptom network and depression symptom network, respectively; (3) “escaping negative moods” for PSU and “concentration problems” for depression were bridge symptoms. The former was maximumly connected with the depression symptoms and the latter was maximumly connected with the PSU symptoms; and (4) gender had very minimal influence on the network characteristics.Conclusion: The results are in keeping with the central idea of the compensatory internet use theory that excessive smartphone use may be a coping strategy for depressed emotions derived from escaping motivation. Moreover, concentration problems may be a mediator explaining how negative emotions (e.g., depression) cause PSU, which is undefined in current internet use theories. Finally, symptom connections, central symptoms, and bridge symptoms could be potential targets for the prevention and intervention of PSU and depression in young adults.

Background: Intolerance of uncertainty (IU) is considered as a specific risk factor in the development and maintenance of generalized anxiety disorder (GAD). Yet, researches have investigated the relations between IU and GAD (or worry) using total scores on self-report measures. This ignores that there are different components exist in IU and the heterogeneity of GAD symptoms. In the present study, we explored the relations among different components of IU and symptoms of GAD.Methods: A dimensional approach which take individual differences into consideration in different components of IU along a full range of normal to abnormal symptom severity levels of GAD were used in this study. Components of IU were measured by 12-item Intolerance of Uncertainty Scale and symptoms of GAD were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Regularized partial-correlation network was estimated using cross-sectional data from 624 university students.Results: Four strongest edges are between components of IU, like “Unforeseen events upset me greatly” and “It frustrates me not having all the information I need”. Two strongest edges are between symptoms of GAD, like “Being so restless that it is hard to sit still” and “Feeling afraid as if something awful might happen”. Symptom “Worrying too much about different things” and component “It frustrates me not having all the information I need” have the highest expected influences in the present network. In the community of IU, component “It frustrates me not having all the information I need” has the highest bridge expected influence. And in the community of GAD, symptoms “Worrying too much about different things” and “Not being able to stop or control worrying” have the highest bridge expected influence.Conclusions: This study reveals potential pathways between different components of IU and various symptoms of GAD. Understanding how putative risk factors such as different components of IU are related to symptoms of GAD may provide some references for related preventions and interventions, such as targeting component “It frustrates me not having all the information I need” may be more effective at reducing symptoms of GAD than targeting other components of IU.

The network perspective of mental disorder offers a novel way of understanding the psychopathology of post-traumatic stress disorder (PTSD). In this framework, PTSD may arise from direct interactions between its symptoms. In the present study, we used the Post-Traumatic Stress Disorder Checklist-civilian Version (PCL-C) to investigate the network structure of PTSD symptoms in 994 Chinese male firefighters. We also calculated the micro (i.e., edges weight and node expected influence) and middle (i.e., community) indicators of the final network. Nine strongest edges existed in the final network were from the same dimension of PCL-C, like "avoidance of thoughts" and "avoidance of reminders". Symptoms "emotional reactivity", "avoidance of reminders" and "exaggerated startle response" had the highest expected influence. As for the results of community detection, the spinglass and walktrap algorithm detected the same three communities which are slightly different from the original dimensions of PCL-C (i.e., symptoms "avoidance of thoughts", "avoidance of reminders" and "trauma-related amnesia" of avoidance dimension of PCL-C were added to the intrusion dimension of PCL-C). The present study explored the network structure of PTSD symptoms in Chinese male firefighters and provided several implications for clinical prevention and intervention to address the mental health needs in this special group.