In this study, we performed a network analysis with depressive symptoms and variables potentially related to depression in subjects screened for depression (N=579). The nodes with high centrality measures identified in regularized partial correlation networks were IESR, GAD, and PHQ_02. It was found that PHQ_01, PHQ_02, PHQ_03, and VA_parents were at the higher part of the Bayesian network. It seems that our results were consistent with the expectation that various variables would be closely linked beyond each domain, and the networks adequately reflect existing medical knowledge. For example, the core symptoms of depression, namely, loss of interest (PHQ_01), and depressed mood (PHQ_02), occupied the higher part in the causal relationship and showed relatively higher centrality scores. In addition, when the connected edges were removed (vulnerability analysis), the five nodes that had the most significant impact on the decrease in the integrity of the entire network were PHQ_09, LSAS, VA_parents, PHQ_07, and VA_peers. Our results suggest that these nodes could become primary treatment target symptoms that can effectively reduce the integrity of the entire psychopathology network of high-DS subjects.
It is well known that depression at a younger age is associated with more anxiety symptoms38. It is also common for these two symptoms to occur together39. Some researchers have even argued that anxiety and depression can be explained by a single factor model3,40. As a matter of fact, the specifier “with anxious distress” was added to the Diagnostic and Statistical Manual for Mental Disorders, 5th Edition (DSM-5)41 to describe the symptoms of anxiety that frequently accompany depression. The fact that patients become anxious when depressed and depressed when anxious is a phenomenon often experienced in clinical settings. Corresponding results were also observed in our study; the symptoms of anxiety and depression were intertwined, according to the results of our network analysis. Impressively, anxiety-related symptoms (IESR, GAD, and LSAS) appeared to be a hub node in the psychopathology networks. The finding was commonly observed in both the regularized partial correlation network and the Bayesian network. Generalized anxiety seemed to act as a bridge node between cognitive/affective (PHQ_01, 02, 06, 09) and somatic factors (PHQ_03, 04, 05, 07, 08) of depression37 in the Bayesian network. GAD was also the node linking subjective distress caused by traumatic events (IESR) and depressive symptoms (PHQ_02, PHQ_06). These findings are consistent with those of previous studies showing that depressed mood and anxiety are associated with somatic symptoms42. In addition, there have been studies on the effects of generalized anxiety disorder, panic disorder, and major depressive disorder on somatic complaints using structural equation modeling43. No direct associations between emotional awareness and somatic complaints were found; however, there were direct associations among depression, anxiety, and somatic complaints. Also, recent studies on heartbeat evoked potential (HEP) have also found that generalized anxiety44 or social anxiety45 were associated with an inadequate increased HEP. These results suggest that anxiety symptoms are associated with abnormally increased somatosensory sensitivity of body sensation. Our study seems to be in line with these findings as well because our network analysis results obtained from young adult subjects can be interpreted as showing psychopathology in which cognitive/affective symptoms spread to somatic symptoms of depression and other associated symptoms of depression through generalized anxiety both in regularized partial correlation network and Bayesian network.
One notable point was that the depressive symptoms were more strongly associated with SAS (smartphone addiction) than CAGE, Smoking in the network of high-DS subjects. In the regularized partial correlation network, SAS was connected to LSAS, Concerns, VA_parents, VA_peers, and PHQ_07. In the Bayesian network, it was connected to Concerns and LSAS. However, CAGE and Smoking were not connected to depression or anxiety symptoms. This may reflect the bias of the college students’ sample. However, some studies have reported that smartphone addiction was related to shyness, loneliness46, low self-esteem, and aggressive behaviors47. These studies commonly mentioned that smartphone addiction might promote the development of depressive disorders. Our study suggests that smartphone addiction might be linked to stresses (the number of concerns, verbal abuses), social anxiety symptoms (LSAS), and concentration problems (PHQ_07). Hence, it could be a facilitating factor for depression in young adults. Contrary to what we have previously known, it may be important to consider that smartphone addiction could be more associated with depressive symptoms than substance addiction in young adulthood depression.
In addition, in our study, we examined changes in the whole network’s topology by comparing it with the intact network after damage to each node. We were able to determine which node was more effective in reducing the integrity of the whole psychopathology network. Inducing a local change in a network and observing a change in the overall topology is different from determining a node’s importance through a centrality measure in an intact network36. The former makes it possible to observe the topology change of the whole variable network due to the change in each node, and the latter only represents the importance of the nodes that make up the whole psychopathology network. The analysis allowed us to identify symptoms that require intervention to reduce the connectivity of the entire disease network. It was expected that if intervention for certain variables was prioritized, such as social anxiety (LSAS), verbal abuse (VA_parents and VA_peers), concentration problems (PHQ_07), and suicidal ideation (PHQ_09), it would be possible to stabilize the overall psychopathological network more efficiently in terms of both global efficiency and clustering coefficient.
The advantage of our study was that we aimed to observe the psychopathology network through various statistical aspects of the network, such as partial correlation network, Bayesian network, and how the topology of the whole network changes when each node of the network is intervened. In addition, rather than using only the scales limited to depression or anxiety, our study had the strength of using various scales, including generalized anxiety, social anxiety, subjective distress due to traumatic events, addiction (alcohol, nicotine, smartphone), the number of concerns, the number of mentors, and perceived verbal abuse. This is expected to be more advantageous than previous analysis using only depression or anxiety symptoms in that both social and environmental factors were included to explain the psychopathology networks. This enabled more appropriate network analysis in that it utilized as many variables as possible that could affect the network of psychopathology. However, our study was still limited in that we used data collected cross-sectionally at a particular time point. To compensate for this limitation, we used not only a graphical Gaussian model but also Bayesian network analysis, which may represent the information of causal relationships because Bayesian network (DAG) analysis is relatively useful in inferring the causal relationship of symptoms in situations where time-series data are not available. However, it is worth noting that the directions of arrows in the DAG does not necessarily indicate causal relationships. The graph from A to B to C and the graph from C to B to A are identical in terms of conditional independence. Certainly, the DAG represents at least the associations between nodes; however, it is challenging to be sure that DAG represents the causal relationships between nodes. In the future, we anticipate that network analysis of psychopathology should be conducted using information gathered at various time points.
In terms of psychopathology, our research revealed that not only variables in a limited domain should be considered significant; rather, variables in multiple domains should be considered comprehensively. In addition, the analysis of the network showed that certain variables were more important in terms of centrality, causal relationship, and the potential to lower the integrity of the network. And the specific variables were not limited to depressive symptoms but encompassed various domains. We suggest that the understanding centered on the hub or the bridge node of the network and the treatment centered on the node that can significantly lower the integrity of the network would be helpful in the diagnosis and treatment of young adults' depression.