Researchers have suggested that major depressive disorder (MDD) is a "nonlinear dynamic phenomenon with two discrete states." If so, then treatment can be more effectively tailored based on which state of MDD patients are in.
A study in BMC Psychiatry suggests that treatment of major depressive disorder (MDD) might benefit from considering the condition as a “nonlinear dynamic phenomenon with two discrete states.” The researchers posit that differentiating between the state of MDD that patients are in, based on their life data, can point the way to safer, more efficacious treatment.
According to the study authors, when MDD is presented schematically, its time course is often displayed as a rectangular on-off-curve, and patients diagnosed with depression report that they experience their normal state and episodes of depression as discrete mood states. This theory has been presented previously, but this is the first study to investigate it empirically. In one study, the authors observe, “critical slowing down as an indicator of nearby tipping points predicted mood shifts in depressed patients. This finding provides some indirect evidence for the presence of alternating stable states in depression.”
The researchers introduced the study with a discussion on systems that have equilibria on several levels and how they typically move between those levels. “If a system can adopt two separate states of behaviour, two discernible modes should be observable in the frequency distribution that displays the temporal behaviour of the system,” the researchers wrote. “If depression and normal mental state constitute two qualitatively different states, then one should find bimodality in the frequency distribution of the symptoms reported over time.”
The current study examined 178 primary care patients with MDD and recorded weekly for 2 years the presence of the nine DSM-IV symptoms of depression. For each patient, the time-series plots as well as the frequency distributions of the symptoms over 104 weeks were inspected. Furthermore, two indicators of bimodality were obtained: the bimodality coefficient (BC) and the fit of a 1- and a 2-state Hidden Markov Model (HMM).
In two-thirds of the patient sample, high bimodality coefficients (BC > .55) were found. These corresponded to relatively sudden jumps in the symptom curves and to highly skewed or bimodal frequency distributions. The results of the HMM analyses classified 90% of the symptom distributions as bimodal.
The results can be interpreted to support the “all or nothing” distinction between depression and health. “On the other hand,” the researchers note, “the results show that bimodality is a matter of degree and that in many patients continuous temporal variations in depression severity also play an important role. These variations are a potentially important source of between-person heterogeneity and might indicate the involvement of different underlying mechanisms.”
Study limitations include the possibility that repeated interviews of study participants may have triggered the switches from one regime to the other in some individual patients. Also, because the information about symptoms was recorded retrospectively, participants may have reported the onset and remission of the symptoms as more or less simultaneously only because it is easier to recall and to report simple patterns than more differentiated or random ones.
The authors hope that the finding leads to larger-scale research that could contribute further to understanding the dynamics of depression.