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

Tenke, Craig E.; Kayser, Jürgen; Pechtel, Pia; Webb, Christian A.; Dillon, Daniel G.; Goer, Franziska; Murray, Leigh; Deldin, Patricia J.; Kurian, Benji T.; McGrath, Patrick J.; Parsey, Ramin V.; Trivedi, Madhukar H.; Fava, Maurizio; Weissman, Myrna M.; McInnis, Melvin G.; Abraham, Karen; Alvarenga, Jorge E.; Alschuler, Daniel M.; Cooper, Crystal; Pizzagalli, Diego A.; Bruder, Gerard E.

doi:10.1111/psyp.12758

Cited by 52 publications

(78 citation statements)

References 110 publications

(213 reference statements)

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“…In comparison to resting theta oscillations, the alpha rhythm dominates the resting EEG, with most individuals showing a distinct alpha peak at about 10 Hz having a robust posterior topography (Aurlien et al, 2004;Chiang, Rennie, Robinson, van Albada, & Kerr, 2011). Moreover, the alpha rhythm is prominent (e.g., visible in raw EEG traces) and reliably quantified by different research groups using different methodologies (e.g., Barry & De Blasio, 2018;Labounek et al, 2018;Schmidt et al, 2017;Shackman et al, 2010;Sockeel, Schwartz, Pélégrini-issac, & Benali, 2016;Tenke et al, 2017). Importantly, greater posterior alpha oscillations at rest predicted a favorable clinical outcome for individuals diagnosed with MDD (Baskaran et al, 2017;Bruder et al, 2008;Jaworska, de la Salle, Ibrahim, Blier, & Knott, 2019;Kandilarova et al, 2017;Knott et al, 1996;Tenke et al, 2011;Ulrich, Renfordt, Zeller, & Frick, 1984;Ulrich, Renfordt, & Frick, 1986; although see Arns et al, 2016, andKnott, Mahoney, Kennedy, &Evans, 2000, for unsuccessful attempts to replicate these findings).…”

Section: Research Findings On Posterior Alphaband Activity As a Canmentioning

confidence: 99%

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

et al. 2019

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Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one lowvariance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.

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Section: Research Findings On Posterior Alphaband Activity As a Canmentioning

confidence: 99%

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

et al. 2019

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“…Solutions for amplitude spectra (rather than power or log power) appeared optimal, and the authors argued for the application of CSD transformations to provide a better model of the underlying brain activity. This CSD/f‐PCA approach has since been applied in relation to the treatment of depression (Tenke et al, ), and further research employing this f‐PCA methodology has also been published (e.g., Kayser et al, ; Tenke, Kayser, Abraham, Alvarenga, & Bruder, ; Tenke, Kayser, Pechtel et al, ; Tenke, Kayser, Svob et al, ).…”

Section: Introductionmentioning

confidence: 99%

EEG frequency PCA in EEG‐ERP dynamics

Barry

Blasio

2017

Psychophysiology

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Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG.

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“…These included resting (task-free) measures of power in the alpha (recorded during eyes-closed and eyes-open) and theta (eyesclosed) bands, loudness dependence of auditory evoked potentials, and theta current density extracted from the rACC [25]. For details regarding paradigms and methods, see Tenke et al [30]. All variables had at least some missing data, ranging from 5.0 to 20.6%.…”

Section: Outcomementioning

confidence: 99%

A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial

Trivedi

South

Rush³

et al. 2018

Psychother Psychosom

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Background: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. Methods: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. Results: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. Conclusion: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.

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

Cited by 52 publications

References 110 publications

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

EEG frequency PCA in EEG‐ERP dynamics

A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial

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