Complex Dynamic Systems in Mood Disorders

Aim 1: To assess multichannel physiologic measures associated with mood disorders. Hypothesis: The overall hypothesis is that nonlinear dynamic analyses will be able to reveal hidden patterns of complexity in each domain of voice, heart rate variability (RR intervals), movement, arousal, and sleep stage data. Aim 2: To assess differences in patterns and complexity of multiple physiological measures in patients with MDD compared to healthy controls. Hypothesis: MDD will be associated with decreased measures of complexity and increased measures of approximate entropy compared to healthy controls. Aim 3: To assess differences in patterns and complexity of multiple physiologic measures in patients with BD compared to healthy controls. Hypothesis: BD will be associated with decreased measures of complexity and increased measures of approximate entropy compared to healthy controls. Aim 4: To assess changes in patterns and complexity of multiple physiologic measures at baseline and after 2 weeks of treatment for patients with MDD and BD. Hypothesis: Measures of complexity will increase and measures of approximate entropy will decrease in the first two weeks of treatment. Aim 5: To assess the relationship between changes in patterns and complexity of multiple physiologic measures and changes in mood state. Hypothesis: Measures of complexity will increase and measures of approximate entropy will decrease, especially for those who are much or very much improved