One speaker this semester, Olu Ajilore, described the findings of his study in that explored phenotypic predictors of relapse following successful antidepressant treatment in late life depression (Taylor et al., 2025). This longitudinal study followed 135 individuals who had late life depression and were in in remission and 69 control individuals over the span of two years. Interestingly, Taylor et al. found that despite being in remission, the late life depression group exhibited many psychological differences to the control group. These differences included residual depressive and anxiety symptoms, greater medical morbidity, and poorer cognitive performance in processing speed and working memory. Unfortunately, out of these remitted patients some experienced relapse. Taylor et al. found phenotypic factors that predicted relapse in these individuals: greater antidepressant treatment insensitivity needed to achieve remission, lower instrumental social support, and greater severity of life stressors.
Similar work to this one has been done on teens. The article in Neuroscience News titled “Early Teen Depression May Be Easier to Treat Than Adults” by Jessica Conway describes a recent finding by Grimes et al. published in Nature Mental Health titled “Network temperature as a metric of stability in depression symptoms across adolescence” in which symptoms of adolescence with early teen depression were monitored using network analysis (Grimes et al., 2025). These researchers used data from over 35,000 teens and applied a novel “network temperature” model that tracked the teens symptoms. This network temperature applied an understanding of how temperature affects matter from physics- as temperature rises, particles move more freely, and the system becomes less stable. They applied this idea to depressive symptoms where the symptoms were nodes and connected in their model. Interestingly, they found that depressive symptoms stabilize with age and become more resistant to change. Furthermore, these early symptoms are more responsive to treatment. They suggested that early adolescence could be a critical window for targeted personalized mental health intervention.
I found it interesting how these two findings both related to each other and contrasted with each other. The biggest contrast was the recurrence of symptoms based on the if the depression was early life or late life. Olu Ajilore’s work found that even though patients are in remission, they still have many psychological differences from control including depression and anxiety symptoms. This demonstrated the more fixed mental state of those in late life depression compared to those with early life depression in which their symptoms were shown to be minimally fixed the earlier that the teen had their symptoms. One way that the articles may not fully align is how in the late life depression article they displayed predictors amongst individuals to avoid relapse, displaying some individual ability to not remain in their previous fixed mental state. One major question that I am left wondering is what would be found if this network analysis technique was applied to adults in late life depression remission? How stable would their symptoms be and could you incorporate phenotypical predictors in this network to predict stability as well?
References
Conway, J. (2025, April 30). Early teen depression may be easier to treat than in adults. Neuroscience News. https://neurosciencenews.com/teen-depression-mental-health-28784/
Grimes, P. Z., Murray, A. L., Smith, K., et al. (2025). Network temperature as a metric of stability in depression symptoms across adolescence. Nature Mental Health. https://doi.org/10.1038/s44220-025-00415-5
Taylor, W. D., Butters, M. A., Elson, D., Szymkowicz, S. M., Jennette, K., Baker, K., Renfro, B., Georgaras, A., Krafty, R., Andreescu, C., & Ajilore, O. (2025). Reconsidering remission in recurrent late-life depression: Clinical presentation and phenotypic predictors of relapse following successful antidepressant treatment. Psychological Medicine, 54(12), 4896–4907. https://doi.org/10.1017/S0033291724003246
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