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

Martijn Arns, Alexander Cerquera, Rafael M. Gutiérrez, Fred Hasselman, Jan A. Freund

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1392-1399
Number of pages8
JournalClinical Neurophysiology
Volume125
Issue number7
DOIs
StatePublished - Jul 2014

Keywords

  • Depression
  • EEG
  • Lempel-Ziv complexity
  • Non-linear analysis
  • Personalized medicine
  • RTMS
  • Signal processing

ASJC Scopus subject areas

  • Sensory Systems
  • Neurology
  • Clinical Neurology
  • Physiology (medical)

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