Back

High frequency oscillations and the 1/f slope vary across a spectrum of depression severity

Hosler, J. G.; Coxon, J.; Hendrikse, J. J.

2025-07-31 neuroscience
10.1101/2025.07.27.667095 bioRxiv
Show abstract

Depression is a highly prevalent mental disorder that impacts an individuals functioning, societal productivity, and quality of life. It is associated with disrupted neural activity (e.g., balance of excitation-inhibition) across networks implicated in emotional processing, such as between prefrontal and limbic regions. High frequency oscillations measured with electroencephalography (i.e., beta and gamma rhythms) differ between healthy individuals and those with depression, and predict treatment outcomes following pharmacological intervention. However, to date research has focussed on binary comparisons between individuals with a clinical depression diagnosis relative to healthy control populations, providing limited insight into how these measures may shift as a function of illness severity. To establish the utility of EEG measures as potential biomarkers for depression, an improved understanding across the spectrum of symptom profiles is required. Here, we aimed to bridge this gap and investigate changes in high frequency beta and gamma oscillations and the 1/f slope in resting-state EEG across a spectrum of mild to severe depression symptom presentations. In line with expectations, we demonstrate graded alterations to gamma and beta power and the 1/f slope across a spectrum of depression severity. Our findings provide critical new insights into the neurophysiological signature of depression symptoms, and highlight the utility of EEG markers to inform future precision psychiatry approaches to more effectively assess and treat depression. HighlightsO_LIHigh frequency brain oscillations are associated with depression severity C_LIO_LIExcitation-inhibition balance is altered in depression C_LIO_LIThese neural markers could inform precision psychiatry approaches C_LI

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.