Literature DB >> 23763894

Baseline and treatment-emergent EEG biomarkers of antidepressant medication response do not predict response to repetitive transcranial magnetic stimulation.

Alik S Widge1, David H Avery, Paul Zarkowski.   

Abstract

There has been a surge of interest in biomarkers that can rapidly predict or assess response to psychiatric treatment, as the current standard practice of extended therapeutic trials is often dissatisfying to both clinicians and patients. Electroencephalographic (EEG) biomarkers in particular have been proposed as an inexpensive yet rapid way of determining whether a patient is responding to an intervention, usually before subjective mood improvement occurs. However, even the most well-reported EEG algorithms have not been subjected to independent replication, limiting their clinical generalizability. It is also unclear whether those biomarkers can generalize beyond their original study population, e.g. to patients undergoing somatic treatments for depression. We report here analysis of EEG data from the pivotal OPT-TMS study of transcranial magnetic stimulation (rTMS) for major depressive disorder. In this dataset, previously reported biomarkers of medication response showed no significant correlation with eventual response to rTMS treatment. Furthermore, EEG power in multiple bands measured at baseline and throughout the treatment course did not correlate with or predict either binary (response/nonresponse) or continuous (Hamilton Rating Scale for Depression) outcome measures. While somewhat limited by technical difficulties in data collection, these analyses are adequately powered to detect clinically relevant biomarkers. We believe this highlights a need for wider-scale independent replication of previous EEG biomarkers, both in pharmacotherapy and neuromodulation.
Copyright © 2013 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Biomarkers; Brain stimulation; Depression; Quantitative electroencephalogram; Transcranial magnetic stimulation

Mesh:

Substances:

Year:  2013        PMID: 23763894      PMCID: PMC3834014          DOI: 10.1016/j.brs.2013.05.001

Source DB:  PubMed          Journal:  Brain Stimul        ISSN: 1876-4754            Impact factor:   8.955


  9 in total

1.  Electroencephalographic and perceptual asymmetry differences between responders and nonresponders to an SSRI antidepressant.

Authors:  G E Bruder; J W Stewart; C E Tenke; P J McGrath; P Leite; N Bhattacharya; F M Quitkin
Journal:  Biol Psychiatry       Date:  2001-03-01       Impact factor: 13.382

2.  Frontal EEG predictors of treatment outcome in major depressive disorder.

Authors:  Dan V Iosifescu; Scott Greenwald; Philip Devlin; David Mischoulon; John W Denninger; Jonathan E Alpert; Maurizio Fava
Journal:  Eur Neuropsychopharmacol       Date:  2009-07-01       Impact factor: 4.600

3.  Changes in prefrontal activity characterize clinical response in SSRI nonresponders: a pilot study.

Authors:  Ian A Cook; Andrew F Leuchter; Melinda L Morgan; William Stubbeman; Barbara Siegman; Michelle Abrams
Journal:  J Psychiatr Res       Date:  2005-09       Impact factor: 4.791

4.  Daily left prefrontal transcranial magnetic stimulation therapy for major depressive disorder: a sham-controlled randomized trial.

Authors:  Mark S George; Sarah H Lisanby; David Avery; William M McDonald; Valerie Durkalski; Martina Pavlicova; Berry Anderson; Ziad Nahas; Peter Bulow; Paul Zarkowski; Paul E Holtzheimer; Theresa Schwartz; Harold A Sackeim
Journal:  Arch Gen Psychiatry       Date:  2010-05

5.  Parieto-temporal alpha EEG band power at baseline as a predictor of antidepressant treatment response with repetitive Transcranial Magnetic Stimulation: a preliminary study.

Authors:  Jean-Arthur Micoulaud-Franchi; Raphaëlle Richieri; Michel Cermolacce; Anderson Loundou; Christophe Lancon; Jean Vion-Dury
Journal:  J Affect Disord       Date:  2012-01-12       Impact factor: 4.839

6.  Neurophysiological predictors of non-response to rTMS in depression.

Authors:  Martijn Arns; Wilhelmus H Drinkenburg; Paul B Fitzgerald; J Leon Kenemans
Journal:  Brain Stimul       Date:  2012-02-22       Impact factor: 8.955

7.  Early reduction in prefrontal theta QEEG cordance value predicts response to venlafaxine treatment in patients with resistant depressive disorder.

Authors:  Martin Bares; Martin Brunovsky; Miloslav Kopecek; Tomas Novak; Pavla Stopkova; Jiri Kozeny; Peter Sos; Vladimir Krajca; Cyril Höschl
Journal:  Eur Psychiatry       Date:  2008-05-02       Impact factor: 5.361

8.  Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in Major Depressive Disorder: results of the BRITE-MD study.

Authors:  Andrew F Leuchter; Ian A Cook; Lauren B Marangell; William S Gilmer; Karl S Burgoyne; Robert H Howland; Madhukar H Trivedi; Sidney Zisook; Rakesh Jain; James T McCracken; Maurizio Fava; Dan Iosifescu; Scott Greenwald
Journal:  Psychiatry Res       Date:  2009-08-27       Impact factor: 3.222

9.  Effectiveness of a quantitative electroencephalographic biomarker for predicting differential response or remission with escitalopram and bupropion in major depressive disorder.

Authors:  Andrew F Leuchter; Ian A Cook; William S Gilmer; Lauren B Marangell; Karl S Burgoyne; Robert H Howland; Madhukar H Trivedi; Sidney Zisook; Rakesh Jain; Maurizio Fava; Dan Iosifescu; Scott Greenwald
Journal:  Psychiatry Res       Date:  2009-08-26       Impact factor: 3.222

  9 in total
  14 in total

Review 1.  Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness.

Authors:  Meng-Chen Lo; Alik S Widge
Journal:  Int Rev Psychiatry       Date:  2017-02-10

Review 2.  Deep Brain Stimulation in Psychiatry: Mechanisms, Models, and Next-Generation Therapies.

Authors:  Mustafa Taha Bilge; Aishwarya K Gosai; Alik S Widge
Journal:  Psychiatr Clin North Am       Date:  2018-07-09

3.  Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

Authors:  Amin Zandvakili; Noah S Philip; Stephanie R Jones; Audrey R Tyrka; Benjamin D Greenberg; Linda L Carpenter
Journal:  J Affect Disord       Date:  2019-03-30       Impact factor: 4.839

4.  Affective Brain-Computer Interfaces As Enabling Technology for Responsive Psychiatric Stimulation.

Authors:  Alik S Widge; Darin D Dougherty; Chet T Moritz
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2014-04-01

5.  Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface.

Authors:  Alik S Widge; Chet T Moritz
Journal:  J Neural Eng       Date:  2014-03-10       Impact factor: 5.379

6.  Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder.

Authors:  Juliana Corlier; Andrew Wilson; Aimee M Hunter; Nikita Vince-Cruz; David Krantz; Jennifer Levitt; Michael J Minzenberg; Nathaniel Ginder; Ian A Cook; Andrew F Leuchter
Journal:  Cereb Cortex       Date:  2019-12-17       Impact factor: 5.357

Review 7.  Building better biomarkers: brain models in translational neuroimaging.

Authors:  Choong-Wan Woo; Luke J Chang; Martin A Lindquist; Tor D Wager
Journal:  Nat Neurosci       Date:  2017-02-23       Impact factor: 24.884

8.  Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis.

Authors:  Alik S Widge; M Taha Bilge; Rebecca Montana; Weilynn Chang; Carolyn I Rodriguez; Thilo Deckersbach; Linda L Carpenter; Ned H Kalin; Charles B Nemeroff
Journal:  Am J Psychiatry       Date:  2018-10-03       Impact factor: 18.112

9.  Transcranial Magnetic Stimulation Indices of Cortical Excitability Enhance the Prediction of Response to Pharmacotherapy in Late-Life Depression.

Authors:  Jennifer I Lissemore; Benoit H Mulsant; Anthony J Bonner; Meryl A Butters; Robert Chen; Jonathan Downar; Jordan F Karp; Eric J Lenze; Tarek K Rajji; Charles F Reynolds; Reza Zomorrodi; Zafiris J Daskalakis; Daniel M Blumberger
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2021-07-23

10.  Spectral asymmetry and Higuchi's fractal dimension measures of depression electroencephalogram.

Authors:  Maie Bachmann; Jaanus Lass; Anna Suhhova; Hiie Hinrikus
Journal:  Comput Math Methods Med       Date:  2013-10-22       Impact factor: 2.238

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