Electrophysiological predictors of clinical response to antidepressants

Bruder, Gerard E.; Tenke, Craig E.; Kayser, Jürgen

doi:10.1017/cbo9781139175869.030

Cited by 11 publications

(20 citation statements)

References 75 publications

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“…Sensitivity of predictions was lower in both our prior and current study suggesting room for improvement. Inclusion of additional behavioral tests, in particular tests of working memory (Gorlyn et al, 2008) or reward learning (Pizzagalli et al, 2005), and electrophysiological (Bruder et al, 2013; Tenke et al, 2011) or neuroimaging measures (Mayberg et al, 1997; Pizzagalli 2011), which have been linked to antidepressant response, might further improve predictions of treatment response. We are currently participating in a multi-site project (EMBARC; Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care), in which behavioral, electrophysiological and neuroimaging measures are being obtained before depressed patients are randomly assigned to treatment with an SSRI or placebo and again after one week of treatment.…”

Section: Discussionmentioning

confidence: 99%

Neurocognitive predictors of antidepressant clinical response

Bruder

Alvarenga

Alschuler

et al. 2014

Journal of Affective Disorders

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Background Executive dysfunction and psychomotor slowing in depressed patients have been associated with poor antidepressant clinical response, but little is known about the value of neurocognitive tests for differential prediction of response. Methods This report presents new findings for 70 depressed patients tested on neurocogntive tests before receiving treatment with a SSRI (escitalopram or citalopram), NDRI (bupropion) or dual mechanism therapy including a serotonergic agent, and for 57 healthy controls. Results As predicted from previous research, patients who did not respond to a SSRI or dual therapy showed poorer word fluency than responders, whereas this was not seen for patients treated with bupropion alone. Longer choice reaction time (RT) was also found in nonresponders to a SSRI or dual therapy, but the opposite trend was seen for bupropion. Using a combined index of word fluency and RT (with normative performance as a cutoff) yielded differential predictions of response. Equal to or above normal performance predicted good response to a SSRI or dual therapy, with high positive predictive value (90%) and specificity (78%) but lower sensitivity (53%). In contrast, less than normal performance predicted good response to bupropion alone (positive predictive value= 82%; specificity= 67%; sensitivity= 90%). Limitations Relatively small sample size, no placebo control, and combining across SSRI alone and dual treatments. Conclusions Although findings are preliminary due to small sample size, brief tests of word fluency and psychomotor speed may help identify depressed patients who are unresponsive to a serotonergic agent, but who may respond to bupropion alone.

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Section: Discussionmentioning

confidence: 99%

Neurocognitive predictors of antidepressant clinical response

Bruder

Alvarenga

Alschuler

et al. 2014

Journal of Affective Disorders

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“…Pre-treatment EEG measures in alpha and theta bands have been repeatedly associated with antidepressant response (Bruder, 2013). Specifically, higher posterior alpha power differentiates patients who respond to antidepressants from those who do not (Bruder et al, 2008; Tenke et al, 2011; Ulrich, Remfordt, & Frick, 1986).…”

Section: Selection Of Electrophysiology Markersmentioning

confidence: 99%

Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design

Trivedi

McGrath

Fava

et al. 2016

Journal of Psychiatric Research

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274

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Remission rates for Major Depressive Disorder (MDD) are low and unpredictable for any given antidepressant. No biological or clinical marker has demonstrated sufficient ability to match individuals to efficacious treatment. Biosignatures developed from the systematic exploration of multiple biological markers, which optimize treatment selection for individuals (moderators) and provide early indication of ultimate treatment response (mediators) are needed. The rationale and design of a multi-site, placebo-controlled randomized clinical trial of sertraline examining moderators and mediators of treatment response is described. The target sample is 400 participants with early onset (<30 years) recurrent MDD. Non-responders to an 8-week trial are switched double blind to either bupropion (for sertraline non-responders) or sertraline (for placebo non-responders) for an additional 8 weeks. Clinical moderators include anxious depression, early trauma, gender, melancholic and atypical depression, anger attacks, Axis II disorder, hypersomnia/fatigue, and chronicity of depression. Biological moderator and mediators include cerebral cortical thickness, task-based fMRI (reward and emotion conflict), resting connectivity, diffusion tensor imaging (DTI), arterial spin labeling (ASL), electroencephalograpy (EEG), cortical evoked potentials, and behavioral/cognitive tasks evaluated at baseline and week 1, except DTI, assessed only at baseline. The study is designed to standardize assessment of biomarkers across multiple sites as well as institute replicable quality control methods, and to use advanced data analytic methods to integrate these markers. A Differential Depression Treatment Response Index (DTRI) will be developed. The data, including biological samples (DNA, RNA, and plasma collected before and during treatment), will become available in a public scientific repository.

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“…Fortunately, there is growing evidence that electrophysiological measures of brain function show potential value as biological markers for predicting subsequent clinical response to antidepressants (Bruder et al, 2013). Of clinical relevance, measures such as the electroencephalogram (EEG) and evoked or event-related potentials (ERPs) provide the advantages of being noninvasive, widely applicable and economical, while providing information about neuronal generator patterns at scalp on a millisecond scale.…”

Section: Electrophysiological Markers For Predicting Antidepressant Tmentioning

confidence: 99%

“…Resting measures of spontaneous brain activity in the alpha and theta bands have shown particular promise as predictors of response to a range of antidepressants (see Alhaj et al, 2011; Bruder et al, 2013 for reviews). Greater alpha power prior to treatment, particularly identifiable at posterior scalp locations, is more likely to be observed in patients who subsequently respond to antidepressants than in nonresponders (Bruder et al, 2008; Prichep et al, 1993; Tenke et al, 2011; Ulrich et al, 1986).…”

Section: Electrophysiological Markers For Predicting Antidepressant Tmentioning

confidence: 99%

“…The demonstration of good-to-excellent reliability for these measures provides a template for future EEG/ERP studies from multiple testing sites, and an important step for evaluating them as biomarkers for predicting treatment response.Despite the availability of pharmacologic treatments for major depressive disorder (MDD), high failure rates for specific treatments can introduce significant delays before relief is obtained from depression. Fortunately, there is growing evidence that electrophysiological measures of brain function show potential value as biological markers for predicting subsequent clinical response to antidepressants (Bruder, Tenke, & Kayser, 2013). Of clinical relevance, measures such as the EEG and evoked or eventrelated potentials (ERPs) provide the advantages of being noninvasive, widely applicable, and economical, while providing information about neuronal generator patterns at scalp on a millisecond scale.Resting EEG.…”

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Demonstrating test‐retest reliability of electrophysiological measures for healthy adults in a multisite study of biomarkers of antidepressant treatment response

et al. 2016

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Growing evidence suggests that loudness dependency of auditory evoked potentials (LDAEP) and resting EEG alpha and theta may be biological markers for predicting response to antidepressants. In spite of this promise, little is known about the joint reliability of these markers, and thus their clinical applicability. New, standardized procedures were developed to improve the compatibility of data acquired with different EEG platforms, and used to examine test-retest reliability for the three electrophysiological measures selected for a multisite project—Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC). Thirty nine healthy controls across four clinical research sites were tested in two sessions separated by about one week. Resting EEG (eyes-open and eyes-closed conditions) was recorded and LDAEP measured using binaural tones (1000 Hz, 40 ms) at five intensities (60–100 dB SPL). Principal components analysis (PCA) of current source density (CSD) waveforms reduced volume conduction and provided reference-free measures of resting EEG alpha and N1 dipole activity to tones from auditory cortex. Low Resolution Electromagnetic Tomography (LORETA) extracted resting theta current density measures corresponding to rostral anterior cingulate (rACC), which has been implicated in treatment response. There were no significant differences in posterior alpha, N1 dipole or rACC theta across sessions. Test-retest reliability was .84 for alpha, .87 for N1 dipole, and .70 for theta rACC current density. The demonstration of good-to-excellent reliability for these measures provides a template for future EEG/ERP studies from multiple testing sites, and an important step for evaluating them as biomarkers for predicting treatment response.

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Electrophysiological predictors of clinical response to antidepressants

Cited by 11 publications

References 75 publications

Neurocognitive predictors of antidepressant clinical response

Neurocognitive predictors of antidepressant clinical response

Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design

Demonstrating test‐retest reliability of electrophysiological measures for healthy adults in a multisite study of biomarkers of antidepressant treatment response

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