Exploring Alpha and Theta Activity in Depression: A Combined Surface EEG and LORETA Study of Cortical and Subcortical Networks
Co-authored study investigating how alpha and theta brain oscillations relate to depressive symptom domains — using resting-state quantitative EEG and source-localized LORETA analysis in adults with depression.
DOI: 10.15540/nr.12.2.89
Authors
Keywords: depression, qEEG, alpha and theta oscillations, hippocampus–amygdala network, frontotemporal area
Summary
Published in NeuroRegulation (pp. 89–97), this study analyzed brain activity in 58 adults with depression using resting-state, eyes-closed qEEG. Alpha (8–12 Hz) and theta (4–8 Hz) absolute power and coherence were examined across 19 scalp electrodes and hippocampal/amygdala regions via LORETA, with symptom severity measured by the Beck Depression Inventory-II (BDI-II).
Alpha coherence between the left hippocampus and amygdala negatively correlated with somatic symptoms (r = −0.298, p = .027), explaining 26% of variance in total BDI-II scores. Increased theta coherence in the right frontotemporal network was associated with reductions in affective and somatic symptoms — suggesting these oscillatory patterns may serve as potential biomarkers and therapeutic targets.
Abstract
Introduction
Depression is a common mental health condition characterized by disrupted neural activity in cortical and subcortical networks involved in emotion and memory. While alpha and theta oscillations have been linked to depression, their specific roles in symptom domains remain unclear. This study examines these relationships using quantitative EEG (qEEG) and low-resolution electromagnetic tomography analysis (LORETA).
Methods
Fifty-eight adults with depression underwent resting-state, eyes-closed qEEG. Absolute power and coherence of alpha (8–12 Hz) and theta (4–8 Hz) bands were analyzed across 19 scalp electrodes and hippocampal and amygdala regions using LORETA. Depressive symptom severity was assessed using the Beck Depression Inventory-II (BDI-II).
Results
Alpha coherence between the left hippocampus and amygdala negatively correlated with somatic symptoms (r = −0.298, p = .027), explaining 26% of variance in total BDI-II scores. Increased theta coherence in the right frontotemporal network was associated with reductions in affective and somatic symptoms.
Conclusions
The findings identify neural oscillatory patterns within hippocampal–amygdala and frontotemporal networks as potential biomarkers for depressive symptoms, providing insights into novel therapeutic targets.