Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression
| Autor: | Raymond W. Lam, Reza Shalbaf, Faranak Farzan, Colleen A. Brenner, Daniel M. Blumberger, Christopher Pang, Fidel Vila-Rodriguez, Joseph C.W. Tham, Zafiris J. Daskalakis, Jonathan Downar |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.medical_specialty
Frequency band medicine.medical_treatment Electroencephalography Audiology Hilbert–Huang transform 03 medical and health sciences 0302 clinical medicine rTMS permutation entropy medicine Pharmacology (medical) EEG empirical mode decomposition Original Research Pharmacology major depressive disorder medicine.diagnostic_test Receiver operating characteristic business.industry lcsh:RM1-950 Area under the curve medicine.disease 3. Good health 030227 psychiatry Transcranial magnetic stimulation Dorsolateral prefrontal cortex lcsh:Therapeutics. Pharmacology medicine.anatomical_structure biomarker business Treatment-resistant depression 030217 neurology & neurosurgery |
| Zdroj: | Frontiers in Pharmacology, Vol 9 (2018) Frontiers in Pharmacology |
| ISSN: | 1663-9812 |
| DOI: | 10.3389/fphar.2018.01188 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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