Depression can be understood as a complex dynamic system where depressive symptoms interact with one another. Cortisol is suggested to play a major role in the pathophysiology of depression, but... Show moreDepression can be understood as a complex dynamic system where depressive symptoms interact with one another. Cortisol is suggested to play a major role in the pathophysiology of depression, but knowledge on the temporal interplay between cortisol and depressive symptoms is scarce. We aimed to analyze the temporal connectivity between salivary cortisol and momentary affective states in depressed individuals and controls. Thirty pair-matched depressed and non-depressed participants completed questionnaires on momentary positive (PA) and negative (NA) affect and collected saliva three times a day for 30 days. The association between cortisol and affect was analyzed by dynamic time warp (DTW) analyses. These analyses involved lag-1 backward to lag-1 forward undirected analyses and lag-0 and lag-1 forward directed analyses. Large inter- and intra-individual variability in the networks were found. At the group level, with undirected analysis PA and NA were connected in the networks in depressed individuals and in controls. Directed analyses indicated that increases in cortisol preceded specific NA items in controls, but tended to follow upon specific affect items increase in depressed individuals. To conclude, at group level, changes in cortisol levels in individuals diagnosed with a depression may be a result of changes in affect, rather than a cause. Show less
Complex systems, from financial markets to the brain, exhibit heterogeneous structures and non-stationary dynamics. These characteristics manifest themselves in the diversity of the elements in a... Show moreComplex systems, from financial markets to the brain, exhibit heterogeneous structures and non-stationary dynamics. These characteristics manifest themselves in the diversity of the elements in a system, and in the changing behaviour over time. Capturing and understanding this heterogeneity via appropriate models, can have important implications not only for science, but also for societal challenges like predicting the next financial crisis or developing advanced brain imaging techniques. In this thesis, we use the maximum-entropy approach to introduce a new class of statistical models, which captures part of the observed structural and/or temporal heterogeneity in the system. The models are applied to various real-world complex systems, and are used to address different problems. Show less
