Unconscious Elevated Bottom-Up Processing in Depression: Insights from Dynamic Causal Modeling with EEG and fMRI

Introduction: MRI compatible EEG systems enable simultaneous EEG-fMRI data assessment, which provides high spatial and high temporal resolution of neural signaling data. Functional connectivity analyses suggest altered fronto-limbic emotion regulation in patients with major depressive disorder (MDD). Methods: Sixty patients with MDD an 66 healthy controls (HC) performed a priming task using unconsciously and consciously presented emotional facial expressions (happy, sad, neutral) performed a priming task using unconsciously and consciously presented emotional facial expressions. Effective connectivity of simultaneously recorded EEG-fMRI data between cortical (bilateral dorsolateral prefrontal cortex and fusiform gyrus) and subcortical regions (bilateral amygdala) was captured using dynamic causal modeling (DCM). Delineate stimulus- related changes in bottom-up and top-down neurophysiological networks across both EEG and fMRI data were estimated in models of unconscious and conscious processing, defined for both groups. Results: Bayesian model selection favored a bottom-up processing model for both groups and input conditions (conscious and unconscious) in EEG-DCMs. Mixed top-down and bottom-up processing models best represented conscious and unconscious stimulus processing in HC fMRI-DCM, while bottom-up models were most representative for MDD fMRI data. Amygdala activity leads to higher DLPFC activity in conscious, and lower DLPFC activity in unconscious conditions in both groups. Conclusion: This study demonstrates the distinct capabilities of EEG and fMRI data through, showing that EEG captures early and fast processing (bottom-up) while fMRI reflect both, bottom-up and top-down regulation. Activity reduction of DLPFC through FFA bottom-up connectivity in early processing (EEG-DCM) might inhibit later top-down emotion regulation through the DLPFC in MDD (fMRI-DCM).