Can Interactions Between Depression Symptoms Help Predict Relapse?
November 13, 2025
Many people living with depression experience periods when their symptoms ease or disappear. Unfortunately, depression can return, sometimes without warning, and clinicians have few tools to predict who is most at risk. A recent CAN-BIND study, published in Psychiatry Research, explored a new approach by examining whether the way depression symptoms interact over time can help identify individuals more likely to relapse, even when they appear to be doing well.
Looking Beyond Symptom Severity
Most approaches to predicting depression relapse focus on how severe a person’s symptoms are at a given time. While symptom severity offers valuable insight, it does not always capture the full picture of relapse risk. In this study, CAN-BIND researchers explored a different perspective by examining how depression symptoms relate to one another over time. Rather than focusing on symptom intensity in isolation, they looked at how symptoms change together and whether these patterns might reveal underlying vulnerability. By viewing depression as a condition shaped by relationships between symptoms rather than separate experiences, the study aimed to identify early warning signs of relapse that may not be obvious when an individual appears to be doing well.
“Historically, depression was thought of as a single latent (unobservable) entity that causes the symptoms that we observe. However, this approach has been increasingly recognized as inadequate in capturing the complexity and heterogeneity of conditions like MDD“
What Is Symptom Network Analysis?
Symptom network analysis is a method that allows researchers to study how mental health symptoms relate to and influence one another over time. Instead of treating each symptom as separate, this approach views depression as a system of interacting symptoms, where changes in one symptom can affect others. This makes it possible to capture patterns that may not be visible when symptoms are examined individually.
In this study, researchers focused on three key features of symptom networks:
- Network density: how strongly symptoms are connected overall. Higher density means changes in one symptom are more closely linked to changes in other symptoms.
- Vertex cover size: how many symptoms are needed to connect to all other symptoms in the network. A larger vertex cover suggests many symptoms play a central role in linking the system together.
- Minimal dominating set size: the smallest group of symptoms that can influence the entire network. A smaller set indicates a few symptoms have a strong influence over many others, while a larger set suggests influence is more spread out.
How Did This Study Work?
The study followed 87 adults with a history of major depression who were in remission at the start. Participants reported their depressive symptoms weekly for up to one year. Using these reports, researchers calculated each person’s symptom network and examined whether the network features could predict the timing of relapse. Relapse was determined using standardized clinical criteria rather than short-term symptom fluctuations.
What Did This Study Reveal?
The results of the study revealed clear differences in relapse risk based on symptom network structure. By looking at how symptoms were connected, the researchers identified patterns that made some individuals more vulnerable:
- Individuals with more tightly connected symptom networks were more likely to relapse sooner.
- Larger vertex cover sizes were also linked to a higher risk of relapse.
- In contrast, individuals with a larger minimal dominating set tended to have a lower risk of relapse, suggesting their symptoms were less driven by a small set of highly influential symptoms.
“…patients with more densely connected symptom networks were more likely to relapse over the follow-up period, and this result was robust to control for baseline symptom severity, perceived stress, and medication status.“
To better understand the mechanisms underlying relapse, the researchers also conducted an exploratory analysis to investigate whether specific symptoms had a stronger influence than others. They found that low mood, pessimism, loss of interest, difficulty concentrating, and thoughts related to self-harm were particularly influential when closely connected to other symptoms in the network.
Why This Matters
This study suggests that relapse risk may not depend solely on how severe symptoms are but on how interdependent they are. In tightly connected symptom networks, a shift in one symptom can more easily trigger changes in others, increasing vulnerability. If these findings are replicated and refined, symptom network analysis could help clinicians:
- Identify individuals at higher risk of relapse earlier.
- Monitor remission and changes in symptoms more precisely over time.
- Target treatment toward symptoms that more strongly influence the overall network
“Predicting these relapses could offer opportunities for early intervention and reduction of total time spent depressed. Despite the critical need, the prediction of relapse remains elusive, potentially due to the heterogeneous presentation of MDD and the limitations of existing predictive models.”
Limitations to Consider
The study included a relatively small sample and focused on individuals already in remission, which may limit how broadly the findings apply to all people with depression. In addition, while weekly symptom reports offered detailed information on how symptoms changed over time, they were self-reported by participants, which may have introduced some reporting variability. Finally, while the study found strong links between symptom network structure and relapse risk, it does not establish that network structure directly causes relapse. Nevertheless, these findings provide valuable insight into how depressive symptoms interact and offer a foundation for future research aimed at improving the prediction and prevention of relapse.
Final Takeaway
Understanding depression relapse as a network of interacting symptoms offers new insights into risk and treatment. By examining how symptoms influence one another rather than focusing only on their severity, symptom network analysis may provide a more sensitive and personalized way to detect early warning signs. With additional research, this approach could help clinicians identify relapse risk sooner and deliver care tailored to each individual’s unique symptom profile.
Citation: Nunes, A., Pavlova, B., Nuñez, J.-J., Quilty, L. C., Foster, J. A., Harkness, K. L., Ho, K., Lam, R. W., Li, Q. S., Milev, R., Rotzinger, S., Soares, C. N., Taylor, V. H., Turecki, G., Kennedy, S. H., Frey, B. N., Rudzicz, F., & Uher, R. (2025). Symptom network connectivity indices as predictors of relapse in major depressive disorder. Psychiatry Research, 350, 116562. https://doi.org/10.1016/j.psychres.2025.116562