Decreased Neuroautonomic Complexity in Men during an Acute Major Depressive Episode: Analysis of Heart Rate Dynamics

Abstract

Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect integrated function may be useful in probing dynamical changes in this syndrome. Increasing evidence supports the conceptual framework that complex variability is a marker of healthy, adaptive control mechanisms and that dynamical complexity decreases with aging and disease. We tested the hypothesis that heart rate (HR) dynamics in non-medicated, young to middle-aged males during an acute major depressive episode would exhibit lower complexity compared with healthy counterparts. We analyzed HR time series, a neuroautonomically regulated signal, during sleep, using the multiscale entropy method. Our results show that the complexity of the HR dynamics is significantly lower for depressed than for non-depressed subjects for the entire night (P<0.02) and combined sleep stages 1 and 2 (P<0.02). These findings raise the possibility of using the complexity of physiologic signals as the basis of novel dynamical biomarkers of depression.