Non-linear EEG analyses predict non-response to rTMS treatment in major depressive disorder

Abstract

Objective: Several linear electroencephalographic (EEG) measures at baseline have been demonstrated to be associated with treatment outcome after antidepressant treatment. In this study we investigated the added value of non-linear EEG metrics in the alpha band in predicting treatment outcome to repetitive transcranial magnetic stimulation (rTMS).

Methods: Subjects were 90 patients with major depressive disorder (MDD) and a group of 17 healthy controls (HC). MDD patients were treated with rTMS and psychotherapy for on average 21 sessions. Three non-linear EEG metrics (Lempel-Ziv Complexity (LZC); False Nearest Neighbors and Largest Lyapunov Exponent) were applied to the alpha band (7-13 Hz) for two 1-min epochs EEG and the association with treatment outcome was investigated.

Results: No differences were found between a subgroup of unmedicated MDD patients and the HC. Non-responders showed a significant decrease in LZC from minute 1 to minute 2, whereas the responders and HC showed an increase in LZC.

Conclusions: There is no difference in EEG complexity between MDD and HC and the change in LZC across time demonstrated value in predicting outcome to rTMS.

Significance: This is the first study demonstrating utility of non-linear EEG metrics in predicting treatment outcome in MDD.

Keywords: Depression; EEG; Lempel–Ziv complexity; Non-linear analysis; Personalized medicine; Signal processing; rTMS.