Network dynamics of depressive symptoms in antidepressant medication treatment: secondary analysis of eight clinical trials


Depression can be viewed as a network of depressive symptoms that tend to reinforce each other via feedback loops. Specific symptoms of depression may be differently responsive to antidepressant treatment, and some symptoms may be more important than others in the overall improvement of depression associated with treatment. We pooled prospective data from eight industry-sponsored placebo-controlled trials for paroxetine, fluoxetine and imipramine (total n = 3559) to examine whether improvements in specific depressive symptoms were more strongly related to improvements in other depressive symptoms among patients on active antidepressant treatment as compared to placebo. Depressive symptoms were assessed with the 17-item Hamilton Depression Rating Scale. Data on treatment was dichotomized into active treatment (receiving any antidepressant agent) vs. placebo. Time-lagged longitudinal analyses suggested that improvement in three symptoms—depressed mood, insomnia, and suicidality—had a broader overall impact on subsequent improvement in other depressive symptoms in the antidepressant condition compared to placebo (i.e., greater out-strength). Moreover, improvements in depressed mood and insomnia were more likely to follow the improvements in other symptoms in the antidepressant condition compared to placebo (i.e., greater in-strength). These results from clinical trial data suggest that depressed mood, insomnia, and suicidality may be particularly important in accounting for the remission and recovery in response to antidepressant treatment.

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Fig. 1: Undirected networks for cross-sectional associations between improvements in depressive symptoms.
Fig. 2: Directed networks for time-lagged longitudinal associations between improvements in depressive symptoms.
Fig. 3: Strength (centrality) estimates of networks comprising cross-sectional associations between symptom improvements.
Fig. 4: Out-strength and in-strength (centrality) estimates of networks comprising time-lagged longitudinal associations between symptom improvements.


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We thank GSK for kindly providing us with data from the included trials, and for enabling access to the data. This study was supported by the Academy of Finland (grant numbers 311578 and 329224).

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Correspondence to Kaisla Komulainen.

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Komulainen, K., Airaksinen, J., Savelieva, K. et al. Network dynamics of depressive symptoms in antidepressant medication treatment: secondary analysis of eight clinical trials. Mol Psychiatry (2020).

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