Neuronal Activation Complexity as a Biomarker Measure for Depression?

The diagnosis of depression is often reliant upon subjective measures and self-report. More recently, psychological researchers have begun employing complexity measures to explore potential stereotypical vs stochastic patterns evident in biological signals over time, which may underlie key features of mental illness. Analyses of neuronal firing across the brain using multiscale entropy (MSE) have been shown to provide an objective report of significant differences between healthy individuals and those afflicted with serious mental and medical illness. However, no study has yet explored the correlation of MSE to sub-clinical levels of depression or cognitive vulnerability factors such as rumination. The current study aimed to determine baseline evidence for a biomarker in the electroencephalogram (EEG) complexity within individuals at varying levels of depression and rumination. EEG data was collected from participants during an eyes-closed resting task and analyzed using an MSE complexity analysis. Results are discussed in relation to current cognitive models of depression