Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression

Reza Shalbaf, Colleen Brenner, Christopher Pang, Daniel M Blumberger, Jonathan Downar, Zafiris J Daskalakis, Joseph Tham, Raymond W Lam, Faranak Farzan, Fidel Vila-Rodriguez, Reza Shalbaf, Colleen Brenner, Christopher Pang, Daniel M Blumberger, Jonathan Downar, Zafiris J Daskalakis, Joseph Tham, Raymond W Lam, Faranak Farzan, Fidel Vila-Rodriguez

Abstract

Background: Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a non-invasive neurophysiological test that has promise as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel non-linear index of resting state EEG activity as a predictor of clinical outcome, and compare its predictive capacity to traditional frequency-based indices. Methods: EEG was recorded from 62 patients with treatment resistant depression (TRD) and 25 healthy comparison (HC) subjects. TRD patients were treated with excitatory repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) for 4 to 6 weeks. EEG signals were first decomposed using the empirical mode decomposition (EMD) method into band-limited intrinsic mode functions (IMFs). Subsequently, Permutation Entropy (PE) was computed from the obtained second IMF to yield an index named PEIMF2. Receiver Operator Characteristic (ROC) curve analysis and ANOVA test were used to evaluate the efficiency of this index (PEIMF2) and were compared to frequency-band based methods. Results: Responders (RP) to rTMS exhibited an increase in the PEIMF2 index compared to non-responders (NR) at F3, FCz and FC3 sites (p < 0.01). The area under the curve (AUC) for ROC analysis was 0.8 for PEIMF2 index for the FC3 electrode. The PEIMF2 index was superior to ordinary frequency band measures. Conclusion: Our data show that the PEIMF2 index, yields superior outcome prediction performance compared to traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression; specifically entropy indices applied in band-limited EEG components. Registration in ClinicalTrials.Gov; identifiers NCT02800226 and NCT01887782.

Keywords: EEG; biomarker; empirical mode decomposition; major depressive disorder; permutation entropy; rTMS.

Figures

FIGURE 1
FIGURE 1
A segment of EEG signal from one participant in FC3 site (A) [X(t)] and EMD of the same segment (B, Imf 1 to Imf 6).
FIGURE 2
FIGURE 2
Scalp topographical maps of PEIMF2 index (resting state, eyes closed). From left to right: topographies of the (A) RP, (B) the NR, and (C) HC. PEIMF2 index in RP and HC groups is higher than NR group especially at left frontal electrodes.
FIGURE 3
FIGURE 3
PEIMF2 index as a function of electrode sites for RP and NR with comparison to HC participant groups. Error bars represent ± 1 standard error. RP differ than NR to rTMS treatment especially at FC3.
FIGURE 4
FIGURE 4
ROC curve analysis and AUC value of Delta, Theta, Alpha, Beta, Gamma relative powers to discriminate between RP an NR on best electrodes. The low AUC value of frequency band measures indicates weak prediction accuracy of these linear approaches.

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