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Objective: Previous studies have found both increased and decreased cortisol levels in depressed patients. These inconsistent findings may be explained by the fact that traditional group-based studies are not suitable to capture complex intra-individual dynamics between cortisol and affect, and inter-individual differences therein. The current study used a time-series approach to gain deeper insight into the nature of these complex dynamics and to investigate possible underlying nonlinear dynamical features.
Method: Time-series data (90 measurements) were collected for cortisol and negative affect (NA) in depressed (n=15) and non-depressed (n=15) participants. The relationship between cortisol and NA in each individual was analyzed with SMAP, which estimates local linear vector autoregression (VAR) models with different degrees of nonlinearity in the prediction. The best-predicting model, and whether this model was linear or nonlinear, was determined by using the normalized root mean square error (NRMSE) between the models’ predicted values and the observed values. Univariate and multivariate models were compared to explore the connection between cortisol and NA.
Results: Nonlinear cortisol predictions were best in 90% of the participants, whereas nonlinear NA predictions were best in 39% of the participants. Multivariate analyses showed that in 48% of the participants, cortisol was better predicted when NA was included in models that otherwise consisted of time delayed values of cortisol alone. Vice versa, in 39% of the participants, NA was better predicted when cortisol was included in models that otherwise consisted of time delayed values of NA alone. The connection between cortisol and NA was stronger in the depressed group, although the results showed considerable inter-individual heterogeneity within the diagnostic groups.
Conclusion: In many individuals, cortisol and NA may be interacting parts of a common dynamical system and their con-nection may be stronger in depressed patients.