Nonlinear Dynamics, Psychology, and Life Sciences, Vol. 20, Iss. 1, Jan, 2016, pp. 1-21 @2016 Society for Chaos Theory in Psychology & Life Sciences Using State Space Methods to Reveal Dynamical Associations Between Cortisol and Depression Abstract: Despite extensive research, the link between etiological factors and depression
remains poorly understood. This may in part be due to a focus on strictly linear definitions of causality,
derived at the group level. However, etiological relations in depression are likely to be dynamical, nonlinear
and potentially unquantifiable with traditional statistics. Therefore the aim of this study was to evaluate
the use of the convergent cross-mapping (CCM) method in investigating possible nonlinear relationships
between supposed etiological factors and depressive symptomatology. Time series data from six healthy individuals
were used to model the relationship between 24-h urinary free cortisol and negative affect using CCM and
dewdrop embeddings. CCM is a nonlinear measure of causality, based on state space reconstruction with
lagged coordinate embeddings. The results showed that nonlinear dynamical relationships between cortisol
and negative affect may be present within participants, as demonstrated by a positive cross-map convergence
from negative affect to cortisol. However, analyses also showed that noise and influential points had
considerable impact on the results. Convergent crossmapping can be used to reveal possible
nonlinear dynamical relationships between etiological factors and psychopathology that may remain undetected
with traditional linear causality measures. Keywords: depressive symptomatology, nonlinear dynamical systems, CCM, time series, negative affect |