Literature DB >> 18751562

Appraisal of sessional EEG features as a correlate of clinical changes in an rTMS treatment of depression.

G W Price1, J W Lee, C Garvey, N Gibson.   

Abstract

Previous findings on electrophysiological features related to depression predict that these correlate with clinical assessment, and potentially act as proxy measures of state changes. We investigated selected electrophysiological features to evaluate their utility as proxies for clinical ratings and in prediction of treatment outcome. Using typical EEG data from an repetitive transcranial magnetic stimulation (rTMS) treatment regime, we analyzed individual alpha power and frequency, and asymmetry index from 39 patients with treatment resistant depression. The prognostic utility of these features was assessed in terms of group identification, correlation with clinical rating, or association with the time course of treatment. There was no significant group difference in asymmetry between depression patients and normal and clinical controls. Background alpha was significantly less in depression patients than controls, with the schizophrenia group midway between. There was no significant group change in asymmetry index or background alpha activity with treatment. There was a weak effect of rTMS over each session on alpha power and on asymmetry, but in the opposite direction to predictions. There was weak evidence of predicted correlation between asymmetry index change and clinical rating change, as well as in final scores that was opposite to predictions. Finally there was no strong evidence that either feature fitted a linear or more complex model of daily treatment. In conclusion, the findings are not sufficient, under our current clinical treatment regime, to support the use of background alpha activity or frontal asymmetry as proxies for clinical assessment. Several findings, however, provide support for further research in this direction.

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Year:  2008        PMID: 18751562     DOI: 10.1177/155005940803900307

Source DB:  PubMed          Journal:  Clin EEG Neurosci        ISSN: 1550-0594            Impact factor:   1.843


  13 in total

1.  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

2.  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

3.  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

Review 4.  Biological markers in noninvasive brain stimulation trials in major depressive disorder: a systematic review.

Authors:  Thiago M Fidalgo; J Leon Morales-Quezada; Guilherme S C Muzy; Noelle M Chiavetta; Mariana E Mendonca; Marcus V B Santana; Oscar F Goncalves; Andre R Brunoni; Felipe Fregni
Journal:  J ECT       Date:  2014-03       Impact factor: 3.635

5.  The EEG as an index of neuromodulator balance in memory and mental illness.

Authors:  Costa Vakalopoulos
Journal:  Front Neurosci       Date:  2014-04-08       Impact factor: 4.677

6.  Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification.

Authors:  Turker Tekin Erguzel; Serhat Ozekes; Selahattin Gultekin; Nevzat Tarhan
Journal:  Psychiatry Investig       Date:  2014-07-21       Impact factor: 2.505

7.  Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance.

Authors:  Turker Tekin Erguzel; Serhat Ozekes; Selahattin Gultekin; Nevzat Tarhan; Gokben Hizli Sayar; Ali Bayram
Journal:  Psychiatry Investig       Date:  2015-01-12       Impact factor: 2.505

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

Authors:  Reza Shalbaf; Colleen Brenner; Christopher Pang; Daniel M Blumberger; Jonathan Downar; Zafiris J Daskalakis; Joseph Tham; Raymond W Lam; Faranak Farzan; Fidel Vila-Rodriguez
Journal:  Front Pharmacol       Date:  2018-10-30       Impact factor: 5.810

Review 9.  Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction? A meta-analysis.

Authors:  Nikita van der Vinne; Madelon A Vollebregt; Michel J A M van Putten; Martijn Arns
Journal:  Neuroimage Clin       Date:  2017-07-15       Impact factor: 4.881

Review 10.  EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies.

Authors:  Jennifer J Newson; Tara C Thiagarajan
Journal:  Front Hum Neurosci       Date:  2019-01-09       Impact factor: 3.169

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