Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures

NW Bailey, KE Hoy, NC Rogasch, RH Thomson, S McQueen, D Elliot, CM Sullivan, BD Fulcher, ZJ Daskalakis, PB Fitzgerald

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: Non-response to repetitive transcranial magnetic stimulation (rTMS) treatment for depression is costly for both patients and clinics. Simple and cheap methods to predict response would reduce this burden. Resting EEG measures differentiate responders from non-responders, so may have utility for response prediction. Methods: Fifty patients with treatment resistant depression and 21 controls had resting electroencephalography (EEG) recorded at baseline (BL). Patients underwent 5–8 weeks of rTMS treatment, with EEG recordings repeated at week 1 (W1). Forty-two participants had valid BL and W1 EEG data, and 12 were responders. Responders and non-responders were compared at BL and W1 in measures of theta (4–8 Hz) and alpha (8–13 Hz) power and connectivity, frontal theta cordance and alpha peak frequency. Control group comparisons were made for measures that differed between responders and non-responders. A machine learning algorithm assessed the potential to differentiate responders from non-responders using EEG measures in combination with change in depression scores from BL to W1. Results: Responders showed elevated theta connectivity across BL and W1. No other EEG measures differed between groups. Responders could be distinguished from non-responders with a mean sensitivity of 0.84 (p = 0.001) and specificity of 0.89 (p = 0.002) using cross-validated machine learning classification on the combination of all EEG and mood measures. Limitations: The low response rate limited our sample size to only 12 responders. Conclusion: Resting theta connectivity at BL and W1 differ between responders and non-responders, and show potential for predicting response to rTMS treatment for depression.

Original languageEnglish
Pages (from-to)68-79
Number of pages12
JournalJournal of Affective Disorders
Volume242
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Alpha
  • Electroencephalography
  • Theta
  • Transcranial magnetic stimulation
  • Treatment resistant depression

Cite this

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title = "Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures",
abstract = "Background: Non-response to repetitive transcranial magnetic stimulation (rTMS) treatment for depression is costly for both patients and clinics. Simple and cheap methods to predict response would reduce this burden. Resting EEG measures differentiate responders from non-responders, so may have utility for response prediction. Methods: Fifty patients with treatment resistant depression and 21 controls had resting electroencephalography (EEG) recorded at baseline (BL). Patients underwent 5–8 weeks of rTMS treatment, with EEG recordings repeated at week 1 (W1). Forty-two participants had valid BL and W1 EEG data, and 12 were responders. Responders and non-responders were compared at BL and W1 in measures of theta (4–8 Hz) and alpha (8–13 Hz) power and connectivity, frontal theta cordance and alpha peak frequency. Control group comparisons were made for measures that differed between responders and non-responders. A machine learning algorithm assessed the potential to differentiate responders from non-responders using EEG measures in combination with change in depression scores from BL to W1. Results: Responders showed elevated theta connectivity across BL and W1. No other EEG measures differed between groups. Responders could be distinguished from non-responders with a mean sensitivity of 0.84 (p = 0.001) and specificity of 0.89 (p = 0.002) using cross-validated machine learning classification on the combination of all EEG and mood measures. Limitations: The low response rate limited our sample size to only 12 responders. Conclusion: Resting theta connectivity at BL and W1 differ between responders and non-responders, and show potential for predicting response to rTMS treatment for depression.",
keywords = "Alpha, Electroencephalography, Theta, Transcranial magnetic stimulation, Treatment resistant depression",
author = "NW Bailey and KE Hoy and NC Rogasch and RH Thomson and S McQueen and D Elliot and CM Sullivan and BD Fulcher and ZJ Daskalakis and PB Fitzgerald",
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Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures. / Bailey, NW; Hoy, KE; Rogasch, NC; Thomson, RH; McQueen, S; Elliot, D; Sullivan, CM; Fulcher, BD; Daskalakis, ZJ; Fitzgerald, PB.

In: Journal of Affective Disorders, Vol. 242, 01.01.2019, p. 68-79.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures

AU - Bailey, NW

AU - Hoy, KE

AU - Rogasch, NC

AU - Thomson, RH

AU - McQueen, S

AU - Elliot, D

AU - Sullivan, CM

AU - Fulcher, BD

AU - Daskalakis, ZJ

AU - Fitzgerald, PB

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Background: Non-response to repetitive transcranial magnetic stimulation (rTMS) treatment for depression is costly for both patients and clinics. Simple and cheap methods to predict response would reduce this burden. Resting EEG measures differentiate responders from non-responders, so may have utility for response prediction. Methods: Fifty patients with treatment resistant depression and 21 controls had resting electroencephalography (EEG) recorded at baseline (BL). Patients underwent 5–8 weeks of rTMS treatment, with EEG recordings repeated at week 1 (W1). Forty-two participants had valid BL and W1 EEG data, and 12 were responders. Responders and non-responders were compared at BL and W1 in measures of theta (4–8 Hz) and alpha (8–13 Hz) power and connectivity, frontal theta cordance and alpha peak frequency. Control group comparisons were made for measures that differed between responders and non-responders. A machine learning algorithm assessed the potential to differentiate responders from non-responders using EEG measures in combination with change in depression scores from BL to W1. Results: Responders showed elevated theta connectivity across BL and W1. No other EEG measures differed between groups. Responders could be distinguished from non-responders with a mean sensitivity of 0.84 (p = 0.001) and specificity of 0.89 (p = 0.002) using cross-validated machine learning classification on the combination of all EEG and mood measures. Limitations: The low response rate limited our sample size to only 12 responders. Conclusion: Resting theta connectivity at BL and W1 differ between responders and non-responders, and show potential for predicting response to rTMS treatment for depression.

AB - Background: Non-response to repetitive transcranial magnetic stimulation (rTMS) treatment for depression is costly for both patients and clinics. Simple and cheap methods to predict response would reduce this burden. Resting EEG measures differentiate responders from non-responders, so may have utility for response prediction. Methods: Fifty patients with treatment resistant depression and 21 controls had resting electroencephalography (EEG) recorded at baseline (BL). Patients underwent 5–8 weeks of rTMS treatment, with EEG recordings repeated at week 1 (W1). Forty-two participants had valid BL and W1 EEG data, and 12 were responders. Responders and non-responders were compared at BL and W1 in measures of theta (4–8 Hz) and alpha (8–13 Hz) power and connectivity, frontal theta cordance and alpha peak frequency. Control group comparisons were made for measures that differed between responders and non-responders. A machine learning algorithm assessed the potential to differentiate responders from non-responders using EEG measures in combination with change in depression scores from BL to W1. Results: Responders showed elevated theta connectivity across BL and W1. No other EEG measures differed between groups. Responders could be distinguished from non-responders with a mean sensitivity of 0.84 (p = 0.001) and specificity of 0.89 (p = 0.002) using cross-validated machine learning classification on the combination of all EEG and mood measures. Limitations: The low response rate limited our sample size to only 12 responders. Conclusion: Resting theta connectivity at BL and W1 differ between responders and non-responders, and show potential for predicting response to rTMS treatment for depression.

KW - Alpha

KW - Electroencephalography

KW - Theta

KW - Transcranial magnetic stimulation

KW - Treatment resistant depression

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U2 - 10.1016/j.jad.2018.08.058

DO - 10.1016/j.jad.2018.08.058

M3 - Article

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JO - Journal of Affective Disorders

JF - Journal of Affective Disorders

SN - 0165-0327

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