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

Ezra E Smith,Randy P Auerbach,Jürgen Kayser,Madhukar H Trivedi,Patricia J Deldin,Diego A Pizzagalli,Myrna M Weissman,Gerard E Bruder,Craig E Tenke

doi:10.1111/psyp.13483

Abstract

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 low-variance 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.

Highlights

Identifying disease‐specific biomarkers that elucidate pathophysiology and predict treatment outcomes is a key target for clinical neuroscience
Two candidate biomarkers that rely on resting EEG activity are currently under investigation as part of a large multisite study (Trivedi et al, 2016): rostral anterior cingulate cortex theta, identified via a current density distributed inverse solution and posterior alpha, identified via scalp current source density (CSD; surface Laplacian) and frequency principal component analysis
A low‐variance, 5 Hz theta component stemming from a combined CSD/eLORETA‐frequency principal component analysis (fPCA) solution of resting EEG was highly similar to midfrontal theta as typically described in the literature (Cavanagh & Shackman, 2015; Schacter, 1977)

Summary

Introduction

Identifying disease‐specific biomarkers that elucidate pathophysiology and predict treatment outcomes is a key target for clinical neuroscience. Two candidate biomarkers that rely on resting EEG activity are currently under investigation as part of a large multisite study (Trivedi et al, 2016): rostral anterior cingulate cortex (rACC) theta, identified via a current density distributed inverse solution (low resolution brain electromagnetic tomography [LORETA]; e.g., Pizzagalli et al, 2001, 2018) and posterior alpha, identified via scalp current source density (CSD; surface Laplacian) and frequency principal component analysis (fPCA; e.g., Tenke et al, 2011). These two biomarkers have demonstrated moderate predictive validity (Pizzagalli et al, 2018; Tenke et al, 2011; Widge et al, 2018), but, in general, published results on EEG biomarkers are biased toward small studies with large effect sizes and positive results (Widge et al, 2018). This study aimed to investigate questions regarding quantification and validity of these two candidate biomarkers

Objectives

Methods

Results

Discussion

Conclusion