Using local linear models to capture dynamic interactions between cortisol and negative affect

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 connection
may be stronger in depressed patients.