Resting electroencephalographic correlates of the clinical response to repetitive transcranial magnetic stimulation: A preliminary comparison between unipolar and bipolar depression

Woźniak-Kwaśniewska, Agata; Szekely, David; Harquel, Sylvain; Bougerol, Thierry; David, Olivier

doi:10.1016/j.jad.2015.04.029

Cited by 33 publications

(27 citation statements)

References 45 publications

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“…This further increased the sensitivity and the chance of a positive meta-analytic result. Articles that did not report sufficient information to reconstruct a 2×2 table (14,15,17,21,25,28,32,33,42,43,81–92) were included in descriptive and study-quality reporting, but not the main meta-analysis.…”

Section: Methodsmentioning

confidence: 99%

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

Widge

Bilge

Montana

et al. 2019

AJP

152

129

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Objective: Reducing unsuccessful treatment trials could improve depression treatment. Quantitative analysis of the electro-encephalogram (QEEG) might predict treatment response, and is being commercially marketed for this purpose. The authors sought to (1) quantify the reliability of QEEG for response prediction in depressive illness and (2) identify methodological limitations of the available evidence. Method: The authors performed a meta-analysis of diagnostic accuracy for QEEG in depressive illness, based on articles published between January 2000 and November 2017. The review included all articles that used QEEG to predict response during a major depressive episode, regardless of patient population, treatment, or QEEG marker. The primary meta-analytic outcome was the accuracy for predicting response to depression treatment, expressed as sensitivity, specificity, and the logarithm of the diagnostic odds ratio (DOR). Raters also judged each article on indicators of good research practice. Results: In 76 articles reporting 81 biomarkers, the meta-analytic estimates showed sensitivity 0.72 (0.67–0.76), specificity 0.68 (0.63–0.73), log(DOR) 1.89 (1.56–2.21), and area under the receiver-operator curve 0.76 (0.71–0.80). No specific QEEG biomarker or specific treatment showed greater predictive power than the all-studies estimate in a meta-regression. Funnel plot analysis suggested substantial publication bias (arcsine asymmetry test, t=6.33, p=2.64e-8). Most studies did not use ideal practices. Conclusions: QEEG does not appear clinically reliable for predicting depression treatment response due to under-reporting of negative results, a lack of out-of-sample validation, and insufficient direct replication of prior findings. Until these limitations are remedied, QEEG is not recommended for guiding psychiatric treatment selection.

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Section: Methodsmentioning

confidence: 99%

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

Widge

Bilge

Montana

et al. 2019

AJP

152

129

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“…This study reuses the data presented in ( Woźniak-Kwaśniewska et al, 2015 ), which contains additional details on the recording conditions. The EEG data were collected in Psychiatry Department of Grenoble University Hospital, after approval by the local ethical committee (ID RCB: 2011-A00114-37).…”

Section: Methodsmentioning

confidence: 99%

“…Considering high temporal resolution of EEG, its low price and easy application, this technique was used very widely to study both the nature of psychiatric disorders and the effect of rTMS. Many investigators have studied the relation between the therapeutic effect of rTMS in depression and spectral power dynamics of various EEG bands ( Pozzi et al, 1993 ; Kwon et al, 1996 ; Griskova et al, 2007 ; Spronk et al, 2008 ; Valiulis et al, 2012 ; Woźniak-Kwaśniewska et al, 2015 ), however, these results were not consistent. Moreover, earlier studies demonstrated that EEG complexity analysis using Higuchi’s fractal dimension (FD) can be successfully used in many clinical applications ( Klonowski et al, 2000 , 2002 , 2004 , 2005 , 2006 ; Olejarczyk, 2007 , 2011 ; Olejarczyk et al, 2009 ; Zappasodi et al, 2014 , 2015 ; Cottone et al, 2016 , 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Complexity Analysis of EEG Data in Persons With Depression Subjected to Transcranial Magnetic Stimulation

et al. 2018

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Aim: The aim of this work was to study the neurophysiological effect of repetitive transcranial magnetic stimulation (rTMS) applied to the left dorsolateral prefrontal cortex (DLPFC) in 8 patients with major depression disorder (MDD) and 10 patients with bipolar disorder (BP), considering separately responders and non-responders to rTMS therapy in each of both groups.Methods: The Higuchi’s Fractal Dimension (FD) was analyzed from 64-channels EEG signals in five physiological frequency bands and every channel separately. Changes of FD were analyzed before and after 1st, 10th, and 20th session of rTMS.Results: Some differences in response to the rTMS therapy was found across individual groups. In MDD responders, FD decreased in all bands after longer stimulation (20th session). Whereas, in BP non-responders, FD decreased after 1st session in all bands as well as after 10th session in lower frequencies (delta and theta). In MDD non-responders and BP responders FD increased at the beginning of the therapy (1st and 10th session, respectively), but the final FD value did not changed in comparison to the initial FD value, except the FD decrease for theta band in BP responders. Comparison between groups showed a higher FD in MDD responders than in MDD non-responders in every band before as well as after stimulation. In contrast to MDD patients, FD was lower in BP responders than in BP non-responders in higher frequency bands (alpha, beta, and gamma) in both conditions as well as in lower frequency bands (delta and theta) after stimulation. Comparing both groups of responders, FD was lower in MDD than in BP in every band, except alpha. In case of non-responders, FD was higher in BP than in MDD in all bands in both conditions.Conclusion: The results showed that FD may be useful marker for evaluation of the rTMS effectiveness and the therapy progress as well as for group differentiation between MDD and BP or between responders and non-responders. The changes of FD under the influence of rTMS allow to unambiguously conclude whether the effect of stimulation is positive or negative as well as allow to evaluate an optimal time of rTMS.

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“…Although research suggests that some therapeutic modalities are more effective than others in ameliorating specific types of symptoms, findings are less clear when patient characteristics beyond diagnosis, symptomatology and symptom severity are considered . Personality traits, styles and disorders may moderate treatment seeking behaviour and treatment outcome for certain presenting problems, but inconsistent findings have emerged in this area as well.…”

Section: Dependency In Psychotherapymentioning

confidence: 99%

Does interpersonal dependency affect therapeutic outcome? A meta‐analytic review

Kane

Bornstein

2019

Personality & Mental Health

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This study examined whether interpersonal dependency affects therapeutic outcome, using meta-analytic techniques to synthesize results from 31 studies (49 effect sizes; overall N = 3807). High levels of dependency were associated with more positive outcome in psychodynamic therapy (r = 0.11, p < 0.05), but not cognitivebehavioural therapy (r = À0.05, ns), and were associated with a less positive outcome in pharmacological treatment (r = À0.15, p < 0.001). Other predictors of outcome included patient diagnosis (high levels of dependency were linked with more positive outcome for patients with anxiety disorders but not those with depression), outcome measure (high levels of dependency were associated with significantly greater improvement in global functioning but not symptom reduction) and dependency measure (cognitive and cognitive-behavioural dependency scales yielded less positive results than other types of measures). These results contribute to a growing body of literature examining effects of personality on treatment process and outcome.

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Resting electroencephalographic correlates of the clinical response to repetitive transcranial magnetic stimulation: A preliminary comparison between unipolar and bipolar depression

Cited by 33 publications

References 45 publications

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

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

Complexity Analysis of EEG Data in Persons With Depression Subjected to Transcranial Magnetic Stimulation

Does interpersonal dependency affect therapeutic outcome? A meta‐analytic review

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