Depressive symptoms and autobiographical memory: A pilot electroencephalography (EEG) study

ABSTRACT

Introduction: Functional magnetic resonance imaging studies have shown changes in the activity of medial prefrontal, medial temporal, and occipital regions in major depressive disorder patients during recall of autobiographical memories. Electrophysiological underpinning of these changes is not known. It is also not clear whether they are a part of the clinical picture or appear at preclinical stages in individuals predisposed to depression.

Method: In this study, the effect of depressive symptoms, as measured by the Beck Depression Inventory (BDI–II), on oscillatory dynamics accompanying retrieval of emotionally positive and negative autobiographical memories was investigated in a nonclinical sample using electroencephalographic event-related spectral power and connectivity measures.

Results: Psychometric results showed that BDI scores correlated positively with the strength of negative emotion, vividness of negative memories, and their importance for participant’s life. In high BDI scorers, low-frequency synchronization, which is frequently used as a marker of emotional arousal, prevailed in negative episodes, whereas in low BDI scorers it prevailed in positive episodes. sLORETA localized sources of this synchronization in the medial prefrontal cortex. In negative episodes, depressive symptoms were associated with a diminished event-related connectivity in the alpha band in posterior regions and increased connectivity in beta and gamma bands in frontal regions.

Conclusions: Overall, these results show that even at preclinical stages, depressive symptoms are associated with changes in electrophysiological processes accompanying retrieval of autobiographical memories.

KEYWORDS:

• Autobiographical memory
• Depression
• Electroencephalography
• Functional connectivity
• sLORETA

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Russian Science Foundation (RSF) under Grant № [14-15-00202] and the Russian Foundation for Basic Research (RFBR) [grant number 14-06-00039]. RFBR supported the development of methods of EEG network analysis; RSF supported the study of depression.